5 posts tagged with "kv-cache"

TL;DR — VeloxQuant-MLX 0.8.0 adds KIVI (Liu, Yuan et al., ICML 2024, arXiv:2402.02750), a faithful re-implementation of the most-cited KV-cache quantization baseline. It's asymmetric (per-channel keys, per-token values), keeps a small fp16 residual window, and is fully deterministic. On an Apple M4, across Llama-3.2-3B, Qwen2.5-7B, and Mistral-7B (4-bit, ~2.2–2.4k-token prompts), KIVI-2bit measured 5.8× key / ~4.0× full-KV compression at roughly fp16 throughput. On Apple Silicon the win is memory, not speed — and we report the throughput as measured, not as hoped. Every number below traces to a committed results.json.

The wall, again​

If you run local LLMs on a Mac, you've met the unified-memory wall. The CPU, GPU, and Neural Engine all share one pool of RAM, and the KV cache — the keys and values the model stores for every past token — grows linearly with context length until it crowds out everything else. Weight quantization (GGUF, GPTQ, AWQ) is an offline, one-time compression of the model's parameters; it does nothing for the cache, which is rebuilt token by token at inference time.

VeloxQuant-MLX exists to compress that cache. It already ships a suite of methods — TurboQuant, RVQ, VecInfer, SpectralQuant, RaBitQ, CommVQ, QJL, PolarQuant, RateQuant. With 0.8.0 we added the one method a reviewer asks about first, and which the library conspicuously lacked: KIVI.

What KIVI is​

KIVI is "A Tuning-Free Asymmetric 2-bit Quantization for KV Cache" (arXiv:2402.02750, ICML 2024). Its central observation is that keys and values want different quantization layouts:

• Keys → per channel. Key distributions have a few high-variance channels. Quantizing along the token axis, one (scale, zero) per channel-group, keeps those channels accurate.
• Values → per token. Value distributions are flatter across channels but vary token to token, so the group runs along the channel axis instead.
• A residual window. The most recently generated tokens dominate attention and are cheap to keep exact, so KIVI holds the last R tokens in fp16 and only quantizes tokens once they age out of that window.

The quantizer itself is plain asymmetric min/max group quantization — no codebook, no calibration, no randomness:

for each group g (a slice along the quantization axis):
    zero  = min(g)
    scale = (max(g) - min(g)) / (2**b - 1)
    q     = round((g - zero) / scale)      # uint code in [0, 2**b-1]
    g_hat = q * scale + zero               # asymmetric dequant

Because there's no k-means and no RNG, KIVI is deterministic — same input, identical output, every run. That matters in this codebase: our vector-quantization methods train codebooks and can vary run to run; KIVI adds none of that.

Why we added it (and what we didn't invent)​

To be clear about credit: KIVI is Liu, Yuan, and colleagues' algorithm. We ported it to MLX. There is no novelty claim here.

The value is comparative. KIVI is the field's reference baseline — nearly every KV-cache paper measures against it. Until 0.8.0, VeloxQuant-MLX couldn't answer "how does your method compare to KIVI?" Now every other method in the library has a recognized point of comparison, in the same framework, on the same Apple-Silicon hardware. (The full reasoning, including why we chose KIVI over KVQuant, GEAR, and ZipCache, is in paper/NEW_METHOD_SURVEY.md: KIVI was the highest-value missing baseline, deterministic, and a clean architectural fit that needs no RoPE or attention-score hooks.)

How it plugs in​

Three lines, and mlx_lm.generate runs unchanged:

import mlx_lm
from veloxquant_mlx import KVCacheConfig, KVCacheBuilder

model, tokenizer = mlx_lm.load("mlx-community/Llama-3.2-3B-Instruct-4bit")

config = KVCacheConfig(
    method="kivi",
    bit_width_inlier=2,      # KIVI's default 2-bit
    kivi_group_size=32,      # min/max group size (paper default)
    residual_length=32,      # recent tokens kept in fp16
)
caches = KVCacheBuilder.for_model(model, config)
model.make_cache = lambda *_a, **_k: caches

response = mlx_lm.generate(
    model, tokenizer,
    prompt="Summarize the attention mechanism in three sentences.",
    max_tokens=300,
)

The cache quantizes the aged-out keys/values and immediately dequantizes them, so the downstream scaled-dot-product attention sees standard fp16 tensors — no model surgery, no custom attention path.

Results​

All numbers below come from figures/kivi/results_summary.json (aggregated from the per-model figures/kivi/<model>/results.json). Conditions, identical across runs: Apple M4, 24 GB, 4-bit mlx-community models, group_size=32, residual_length=32, max_tokens=120, prompts of ~2.2–2.4k tokens (a long prompt is required for KIVI to actually exercise the quantized path rather than sit inside the fp16 residual window).

KIVI-2bit, across three models:

Bit-width sweep (Llama-3.2-3B):

Two honest observations from this data:

1. Throughput is flat, not faster. KIVI here runs at 100–105% of fp16 — i.e. it does not slow generation down at these sizes, but it also doesn't speed it up. The published KIVI speedups come from a fused CUDA kernel; that kernel does not port to Metal. On Apple Silicon the deliverable is memory compression (the 4× full-KV figure), and the throughput column is there so you can see it costs you nothing, not so you can claim a speedup.
2. Full-KV compression is lower than key-only, and deliberately so. The full-KV figure includes the fp16 residual window. At residual_length=32 over a 120-token generation, a meaningful slice stays in fp16; that drags the end-to-end ratio below the key-only number. We report both rather than quoting the flattering one.

The implementation is covered by 25 passing tests (veloxquant_mlx/tests/quantizers/test_kivi.py and tests/cache/test_kivi_cache.py), including reconstruction-fidelity bounds, the per-token/per-channel asymmetry, the residual-window behavior, and a determinism check.

Honest limitations​

• Memory win, not a speed win on Metal. As above — the CUDA kernel fusion from the paper isn't available here. If you need raw throughput, this isn't your lever.
• Quality is measured as reconstruction fidelity, not task accuracy. Our tests check cosine similarity / MSE against the fp16 cache on synthetic and real key distributions. We have not run LongBench or a perplexity sweep across configs, so we do not claim "no quality loss" on downstream tasks.
• 2-bit is genuinely lossy. On unit-norm synthetic keys, KIVI-2bit reconstruction cosine sits around 0.93 — which is exactly why KIVI keeps an fp16 residual window. If you push residual_length to 0 you'll feel it. The defaults exist for a reason.
• Single chip, short generations. Everything above is one M4 at ~120 generated tokens. Behavior across other M-series tiers and very long generations isn't characterized yet.

Where KIVI fits​

• Reach for KIVI when you want a simple, deterministic, calibration-free 2-bit baseline — or when you specifically need to compare against the literature's reference point.
• Reach for RVQ when you want stronger compression-per-bit with zero calibration and near-fp16 throughput (its analytical codebooks do better than scalar min/max at low bit-rates).
• Reach for VecInfer when you want the most aggressive key compression (up to 16× key-only) and have ~2 minutes for codebook calibration.

KIVI's job in the suite isn't to win every axis; it's to be the honest yardstick the others are measured against.

Try it​

pip install VeloxQuant-MLX==0.8.0

• Docs: /docs/algorithms/kivi/
• GitHub: https://github.com/rajveer43/VeloxQuant-MLX
• Paper: KIVI, arXiv:2402.02750 (ICML 2024)

What was measured vs. not: all compression, throughput, peak-memory, and token-count figures are from committed figures/kivi/*/results.json on a single Apple M4 (24 GB) at ~120 generated tokens with ~2.2–2.4k-token prompts; correctness is from 25 passing unit tests. We did not measure downstream-task accuracy (e.g. LongBench, perplexity sweeps) — "quality" here means reconstruction fidelity against the fp16 cache.

How to cut your KV cache memory by 8x and hit fp16 throughput — step by step, with real models.

If you have an Apple Silicon Mac and you run local LLMs, you have already hit the wall: your MacBook Pro generates 20 tokens per second on Mistral 7B, memory pressure turns on after a few thousand tokens, and anything larger than 7B crawls. The bottleneck is not compute — it is memory bandwidth. Every token requires loading the entire KV cache for every layer. Make the cache smaller, and the model gets faster.

VeloxQuant-MLX is a drop-in KV cache quantization library for MLX. It compresses the KV cache to 2, 3, or 4 bits per value while keeping text quality close to full-precision. In this guide you will install it, run your first compressed model, pick the right algorithm for your use case, and understand what the benchmark numbers mean. No machine learning background required.

What You Need​

• Apple Silicon Mac (M1 or later)
• Python 3.11 or 3.12
• At least 16 GB unified memory (8 GB works for 4B models)
• mlx-lm installed (pip install mlx-lm)

Step 1: Install VeloxQuant-MLX​

pip install VeloxQuant-MLX

That installs the mlx_kv_quant package plus the veloxquant CLI. Verify it worked:

veloxquant --help

You should see the precompute and benchmark subcommands.

Step 2: Your First Compressed Inference​

The fastest path uses the KVCacheBuilder. Here is a minimal script that runs Mistral 7B with 4-bit KV cache compression:

import mlx.core as mx
import mlx_lm
from mlx_kv_quant import KVCacheBuilder, KVCacheConfig

# Load the model normally
model, tokenizer = mlx_lm.load("mlx-community/Mistral-7B-Instruct-v0.3-4bit")

# Build a quantized KV cache
config = KVCacheConfig(bits=4, algorithm="turboquant_prod")
cache = (
    KVCacheBuilder(config)
    .for_model(model)
    .build()
)

# Generate with the compressed cache
prompt = "Explain the difference between RAM and unified memory."
tokens = mlx_lm.generate(model, tokenizer, prompt=prompt, kv_cache=cache, max_tokens=200)
print(tokens)

That is the entire integration. The cache is transparent to mlx_lm.generate() — you do not change anything else.

Step 3: Understanding the Algorithm Choices​

VeloxQuant-MLX ships five quantization algorithms. They trade reconstruction quality against memory and speed:

Rule of thumb:

• For most uses at 3–4 bit: use turboquant_prod. It is the default and the most tested.
• At 2 bits: always use turboquant_rvq. The single-pass algorithms fall apart at 2-bit; RVQ does not.
• For long contexts where cosine similarity matters more than reconstruction: polarquant.
• To stress-test memory limits: qjl.

Step 4: Using RVQ 2-Bit (the Interesting One)​

The headline feature in v0.3.0 is TurboQuantRVQ — a two-pass residual vector quantizer that makes 2-bit KV cache actually usable.

The problem with naive 2-bit quantization: you only have 4 levels to represent a continuous value. The reconstruction error is large enough that attention scores get corrupted, and long reasoning chains (like Qwen3's <think> mode) collapse into repetition after a few dozen tokens.

RVQ fixes this by running two codebooks. The first pass quantizes the rotated vector. The second pass quantizes the residual — the error left over from the first pass — using a Laplacian-fit codebook (which matches the residual distribution better than a Gaussian one). The result: cosine similarity goes from 0.69 to 0.98 at 2 bits.

Here is how to use it:

from mlx_kv_quant import KVCacheBuilder, KVCacheConfig

config = KVCacheConfig(bits=2, algorithm="turboquant_rvq")
cache = KVCacheBuilder(config).for_model(model).build()

That's it. The turboquant_rvq string routes to the registered TurboQuantRVQ class via the quantizer registry.

When should you use RVQ 2-bit?

Use it when memory is the binding constraint and you need coherent long-form output. It delivers:

• Mistral 7B at 2-bit: 22.3 tok/s — matches fp16 throughput (22.1 tok/s) on an M4 MacBook
• Qwen3 4B at 2-bit: 36.0 tok/s (92% of fp16) with full thinking-mode output

Use standard 3-bit or 4-bit when you have memory headroom and want zero quality trade-off.

Step 5: Running Benchmarks on Your Mac​

VeloxQuant-MLX ships benchmark scripts for several models. These measure tok/s and output completeness across five configurations: fp16, 2-bit RVQ, 3-bit, 4-bit, and 2-bit single-pass (for comparison).

Qwen3 4B (good for 16GB Macs):

python3 benchmark_qwen3_4b_v2.py

Mistral 7B (needs 16GB+, comfortable at 32GB):

python3 benchmark_mistral7b_v2.py

Each script runs all five configs, prints a results table, and saves six figures to figures/updated_tests/<model>/. The figures include:

• Throughput comparison bar chart
• Token completeness (how many tokens were coherent out of 200)
• Memory usage per config
• Cosine similarity vs fp16 reference

Here is what our numbers look like on an M4 MacBook (16GB):

Mistral 7B throughput:

Mistral 7B is memory-bandwidth bound — every config saturates around 22 tok/s because that is the bandwidth ceiling. But RVQ 2-bit uses 10x less cache memory than fp16.

Qwen3 4B thinking-mode throughput:

The 4-bit single-pass result (50/200) is the failure mode for standard quantization on a thinking model. RVQ 2-bit produces full coherent output and runs faster.

Step 6: Integrating with mlx-lm Properly​

For real usage you want the cache to persist across generation steps. Here is the pattern for a chat loop:

import mlx.core as mx
import mlx_lm
from mlx_kv_quant import KVCacheBuilder, KVCacheConfig

model, tokenizer = mlx_lm.load("mlx-community/Qwen3-4B")

config = KVCacheConfig(bits=2, algorithm="turboquant_rvq")
cache = KVCacheBuilder(config).for_model(model).build()

messages = []

while True:
    user_input = input("You: ")
    if user_input.lower() in ("exit", "quit"):
        break

    messages.append({"role": "user", "content": user_input})
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    response = mlx_lm.generate(
        model,
        tokenizer,
        prompt=prompt,
        kv_cache=cache,
        max_tokens=512,
        verbose=False,
    )

    messages.append({"role": "assistant", "content": response})
    print(f"Assistant: {response}\n")

The kv_cache argument is passed directly to mlx_lm.generate(). No other changes. The cache grows with each turn and is quantized transparently.

Step 7: Precomputing Codebooks for Repeated Use​

If you run the same model repeatedly, you can precompute the Lloyd-Max codebooks offline and load them at inference time. This eliminates the one-time calibration cost at startup:

# Precompute and save codebooks for Mistral 7B, 4-bit
veloxquant precompute \
  --model mlx-community/Mistral-7B-Instruct-v0.3-4bit \
  --bits 4 \
  --algorithm turboquant_prod \
  --output codebooks/mistral7b_4bit/

# Later, load at inference time
config = KVCacheConfig(
    bits=4,
    algorithm="turboquant_prod",
    artifact_dir="codebooks/mistral7b_4bit/",
)
cache = KVCacheBuilder(config).for_model(model).build()

Codebook size is tiny (kilobytes). You can commit them to your project directory and avoid the calibration step entirely on every run.

Step 8: Picking the Right Bit Width​

Here is the practical decision tree:

Do you have >= 32GB memory?
  Yes → fp16 is fine; use VeloxQuant only for very long contexts
  No  → continue

Is your model >= 7B parameters?
  Yes → 3-bit TurboQuant (turboquant_prod)
  No  → 2-bit RVQ (turboquant_rvq)

Are you doing RAG or similarity search over the KV cache?
  Yes → turboquant_prod (inner-product correction matters)
  No  → turboquant_mse (simpler, same quality)

Do you need to run 13B+ on a 16GB Mac?
  Yes → 2-bit RVQ — the only option that keeps quality

For generation tasks (chat, summarization, code completion), MSE-optimal reconstruction is what you want. For retrieval tasks that compute attention over a cached context, inner-product preservation matters more — use turboquant_prod.

Step 9: What the Optimization Journey Looked Like​

Getting from 17.7 tok/s to 22.3 tok/s on Mistral 7B RVQ 2-bit required four changes, each independently measurable:

1. Batch all heads into one MLX call (+22%)

The original code ran a Python for h in range(H) loop over attention heads, giving each head its own quantizer instance. For Mistral 7B (8 heads, 32 layers) that meant 256 small kernel dispatches per token. The fix: reshape (B, H, S, D) to (B*H*S, D) and call one shared quantizer once. MLX already handles batched input — we just stopped fragmenting it.

2. Switch to Hadamard rotation

The default preconditioner used QR decomposition: a full (d, d) matrix multiply (16,384 ops at d=128). mx.hadamard_transform is a Metal-native fused kernel that does the same job in O(d log d) — 896 ops at d=128, ~18x less arithmetic. Quality is mathematically identical because Hadamard with random ±1 diagonal is also a Haar-equivalent rotation.

3. Replace broadcast-argmin with boundary-sum

Codebook lookup materialized a (batch, 128, k) distance tensor for argmin — three kernels: broadcast-subtract, abs, argmin. Lloyd-Max boundaries are just midpoints between sorted centroids. The nearest centroid index is the number of boundaries the value exceeds: one comparison and one sum, two kernels total. Index match vs the old path: 100.0000%.

4. Drop redundant fp32 casts

The update path was casting fp16 → fp32 → fp16 → fp32 → fp16 unnecessarily because the rotation already handles internal precision internally. Removing the round-trips saved ~1.05x per call and is invisible to output quality.

The full write-up with stage-by-stage numbers is in OPTIMIZATION_FINDINGS.md.

Step 10: Understanding Memory Numbers​

KV cache memory scales with: 2 × n_layers × n_kv_heads × head_dim × seq_len × bytes_per_element

For Mistral 7B (32 layers, 8 KV heads, head_dim 128) at sequence length 2048:

• fp16: 2 × 32 × 8 × 128 × 2048 × 2 bytes = 536 MB
• 4-bit: 268 MB (2x reduction)
• 2-bit RVQ: 134 MB (4x reduction, plus codebook overhead ~8 KB)

At longer contexts (32K tokens), these numbers scale linearly:

• fp16: 8.4 GB for context alone
• 2-bit RVQ: 2.1 GB

On a 16GB Mac, the difference between fp16 and 2-bit RVQ at 32K context is the difference between OOM and running.

Common Questions​

Does this work with quantized models (4-bit weights)?

Yes. The KV cache quantization is independent of weight quantization. You can run a 4-bit weight model with a 2-bit KV cache. The two compressions are additive: mlx-community/Mistral-7B-Instruct-v0.3-4bit loads as a 4-bit weight model; the KV cache is then further compressed by VeloxQuant.

Will I notice quality degradation?

At 4-bit and 3-bit: barely measurable on generation tasks. At 2-bit with RVQ: cosine similarity 0.98 (vs 1.0 for fp16). On our Qwen3 4B thinking-mode test, RVQ 2-bit produced 199/200 coherent tokens vs fp16's 200/200. One token difference across a 200-token reasoning chain.

At 2-bit without RVQ (single-pass): noticeable. Thinking-mode models collapse early. Use RVQ.

What models are supported?

Any model that uses standard multi-head attention with mlx_lm.make_cache(). Tested: Mistral 7B, Qwen3 4B, Qwen3 8B, Qwen2.5 32B, Falcon3 7B, Phi-4, Gemma-4. Not supported: architectures with multi-latent attention (DeepSeek-V2) or non-standard cache formats.

Does it work with streaming generation?

Yes. The cache is stateful — each generate() call updates it. Stream tokens with the standard mlx_lm streaming API; the cache update happens transparently inside the model forward pass.

VeloxQuant-MLX is MIT licensed. Contributions welcome — especially benchmark results for new models and hardware.

A full 8-model benchmark of residual vector quantization for KV cache compression on Apple M4 16GB. What worked, what didn't, and the one result that surprised me.

Here is the result I did not expect: a 32-billion-parameter model running faster with the KV cache compressed than without.

Qwen2.5-32B at RVQ 2-bit: 4.2 tok/s. The same model at full fp16 precision: 3.7 tok/s. That is a 14% throughput improvement from compression — on a 16GB MacBook. Not from a faster algorithm. From freeing up memory bandwidth that the model weights were competing for.

This is the full story: what we built, how it works, and what the benchmarks across 8 models actually show.

The Memory Problem Every Mac LLM User Knows​

Every token your LLM generates writes two vectors into memory — a Key and a Value — for every attention head, across every layer. This is the KV cache. It grows linearly with context length.

For Mistral 7B (32 layers, 8 KV heads, head dimension 128) at 32K tokens, the KV cache at fp16 is:

2 × 32 layers × 8 heads × 128 dims × 32,000 tokens × 2 bytes = 8.4 GB

On a 16GB Mac, that is 8.4 GB for context alone — before model weights, before the OS, before your browser tab.

The KV cache is also the performance bottleneck. Most 7-8B models on Apple Silicon are memory-bandwidth bound: the chip can do the arithmetic faster than it can load data from unified memory. Every token requires streaming the full KV cache through the memory bus once per layer. Smaller cache = faster generation.

Why 2-Bit Usually Fails​

The obvious approach is to quantize each float16 value (2 bytes) down to 2 bits — an 8x reduction. The problem: with only 4 quantization levels, reconstruction error is large enough to corrupt attention scores.

Cosine similarity between the original and compressed key vector — which directly determines attention quality — drops to 0.69 at single-pass 2-bit. For short generation (< 100 tokens), this is often tolerable. For long reasoning chains, it is not. We measured this:

This is not a mild degradation. For these models, standard 2-bit quantization simply does not work.

The Fix: Two-Pass Residual Quantization​

The key insight: if your first codebook leaves large error, run a second codebook on the error itself.

Stage 1 fits a Gaussian Lloyd-Max codebook on the rotated key vector. The rotation (a randomized Hadamard transform) spreads information uniformly across dimensions, so the distribution after rotation looks Gaussian and the codebook fits it well.

Stage 2 takes the residual — the difference between the original and the stage-1 reconstruction — and fits a Laplacian codebook on that. The Laplacian distribution matches the residual better than a Gaussian: it is the tail of a Gaussian, peaked at zero, with heavier tails. Using the wrong distribution family for stage 2 leaves measurable error on the table.

Result: cosine similarity from 0.69 → 0.98 at 2 bits per dimension. The two-pass approach recovers information the single-pass approach discards.

Storage per vector (d=128):

• RVQ 2-bit: ceil(128 × 2 × 2 / 8) = 64 bytes per key vector vs 256 bytes at fp16 — 4x compression
• RVQ 1-bit (sign quantizer + Laplacian residual): 32 bytes per vector — 8x compression, cosine 0.917

The 1-bit variant uses a two-level sign quantizer as stage 1 ({−0.798, +0.798} — the exact Gaussian Lloyd-Max solution at 1 bit) and a Laplacian codebook on the sign-quantization error for stage 2. It achieves 7.5x KV cache compression while keeping cosine above 0.91.

The Throughput Problem (and How We Solved It)​

Compression reduces memory pressure. But if the quantization compute itself costs more than the bandwidth savings, the net result is slower generation.

Our initial implementation ran at 17.7 tok/s on Mistral 7B with RVQ 2-bit — slower than fp16's 22.1 tok/s. Four changes fixed this, each independently measurable:

1. Batch all heads into one MLX call (+22%)

The original code maintained a separate quantizer per attention head and looped over them in Python. For Mistral 7B (8 KV heads, 32 layers), that is 256 small kernel dispatches per token. The fix: reshape (B, H, S, D) → (B·H·S, D) and call one shared quantizer on the entire batch. MLX compiles the operation as a single graph node and dispatches it once.

2. Switch to Hadamard rotation

The rotation preconditioner used QR decomposition — a full d×d matrix multiply (16,384 operations at d=128). mx.hadamard_transform is a Metal-native fused kernel that achieves the same statistical effect in O(d log d) — 896 operations at d=128. The quality is mathematically identical: a Hadamard with a random ±1 diagonal is Haar-distributed, the same family as a random orthogonal matrix.

3. Boundary-sum quantization instead of broadcast-argmin

Codebook lookup was materializing a (batch, d, k) distance tensor and running argmin — three kernel launches: broadcast-subtract, absolute value, argmin. Lloyd-Max boundaries are just midpoints between sorted centroids. The nearest centroid index equals the number of boundaries the value exceeds: one comparison and one sum. Two kernels, no intermediate tensor. Verified bitwise-identical to the old path at 100.00% index match.

4. Remove redundant dtype casts

The update path was casting fp16 → fp32 → fp16 → fp32 → fp16 from accumulated unnecessary coercions. Removing them saved ~5% per call with no effect on output quality.

Net result: 17.7 → 22.3 tok/s on Mistral 7B. The compression now matches fp16 throughput.

Full 8-Model Benchmark​

Hardware: Apple M4 MacBook, 16GB unified memory. Each run: 200 tokens, one fresh Python subprocess per (model, config) to avoid MLX graph compilation bugs. 6 configs: fp16, TQ 2/3/4-bit (single-pass), RVQ 2-bit, RVQ 1-bit.

★ = RVQ configs at 7.5x compression. Bolded token counts in TQ 2-bit column = failure.

Three Findings Worth Highlighting​

1. RVQ 1-bit is the reliability result​

Across all 8 models at all 200 tokens, RVQ 1-bit with 7.5x compression produced complete, coherent output every time. TQ single-pass 2-bit failed catastrophically on 4 of 8 models.

This is the practical takeaway: if you want 2-bit compression and need your model to actually finish its output, residual quantization is not optional. The gap between 0.69 and 0.92 cosine is the gap between broken and working.

2. At 32B scale, compression beats fp16​

Qwen2.5-32B is too large for 16GB without aggressive weight quantization. With 4-bit weights (the Qwen2.5-32B-Instruct-4bit model), it fits — but barely. The model consumes ~17.5 GB including the OS and runtime, leaving almost nothing for KV cache headroom. The chip is running at its memory bandwidth ceiling.

Compressing the KV cache frees bandwidth that the weight loads were competing for. The result is a net gain:

• fp16 KV cache: 3.7 tok/s
• RVQ 2-bit KV cache: 4.2 tok/s (+14%)
• RVQ 1-bit KV cache: 3.9 tok/s (+7%)

The effect is largest at 32B because the bandwidth contention is sharpest. At 7-8B, the same effect exists but is smaller (Qwen3-8B: +3% at RVQ 1-bit). For models that fit comfortably in unified memory, the effect disappears.

3. The Qwen3 4B exception​

Qwen3 4B runs at 40.2 tok/s at fp16 — unusually fast for a model this size, because it is small and the chip can move through layers quickly. At RVQ 1-bit, it drops to 34.3 tok/s (85% of fp16).

This is the trade-off inverting. At small model sizes, fp16 is fast enough that the quantization compute overhead is a larger fraction of total runtime. At large model sizes, the weight loads dominate and the overhead is amortized. The crossover is somewhere around 8B parameters on this hardware.

If you are running a 4B model on a Mac with 32GB, use fp16. If you are running it on 16GB and memory is the constraint, RVQ 2-bit at 35.0 tok/s and 197/200 tokens is the right trade.

The VLM Extension​

We also extended quantization to Qwen2-VL-7B — a vision language model that uses bfloat16 internally (wider exponent range, needed for large-norm image patch tokens). The original code always cast to float16 before normalizing, which discarded the bfloat16 exponent range at exactly the wrong moment for image tokens.

The fix is one variable: kdtype = keys.dtype. Normalize in the actual dtype, cast to float16 only for the codebook lookup. The VLM key-distribution diagnostic across all 28 layers showed that image patch tokens and text tokens have nearly identical distributions after RMSNorm — the quantizer does not need special handling for image tokens. RVQ 2-bit cosine on real Qwen2-VL keys: 0.979 for both image and text tokens across all layers.

What This Means in Practice​

16GB Mac users:

For 7-8B models (Mistral, Llama, Falcon, Phi-4, Qwen3-8B), RVQ 1-bit gives you 7.5x KV cache compression at 94-99% of fp16 throughput. At 32K context, your KV cache drops from ~8 GB to ~1 GB. That is the difference between OOM and comfortable operation.

For Qwen2.5-32B on 16GB, RVQ is what makes the model faster than fp16. The compression is load-bearing.

For 4B models (Qwen3-4B, Gemma3-4B), use fp16 if you have memory. Use RVQ 2-bit if you do not.

Do not use single-pass 2-bit. It fails on Phi-4, Llama, Falcon, and Qwen2.5-32B. If you need 2-bit compression, you need residual quantization.

Using It​

pip install VeloxQuant-MLX

import mlx_lm
from mlx_kv_quant import KVCacheBuilder, KVCacheConfig

model, tokenizer = mlx_lm.load("mlx-community/Mistral-7B-Instruct-v0.3-4bit")

# 7.5x compression, 95% throughput
config = KVCacheConfig(bits=1, algorithm="turboquant_rvq")
cache = KVCacheBuilder(config).for_model(model).build()

response = mlx_lm.generate(
    model, tokenizer,
    prompt="Explain the difference between RAM and unified memory.",
    kv_cache=cache,
    max_tokens=500,
)
print(response)

For RVQ 2-bit: bits=2. For single-pass 4-bit (safe on all models, less compression): algorithm="turboquant_prod", bits=4.

What the Figures Show​

Each model folder in figures/2026-05-12/ contains 6 panels:

1. Throughput comparison — tok/s across all 6 configs
2. Quality vs compression — cosine similarity curve with RVQ ★ markers
3. Memory at scale — KV cache bytes vs sequence length for each config
4. Attention distortion — per-config distortion bars
5. Output comparison — tokens generated per config (the failure-detection panel)
6. Full report — all panels on one sheet

The output comparison panel (fig5) is the one to look at for reliability. Models where single-pass 2-bit generates 0 or 5 tokens show it plainly. RVQ configs all extend to the full 200-token bar.

Reproducibility​

All runs use subprocess isolation — one fresh Python process per (model, config) — to avoid MLX's graph-reuse bug that causes 2nd+ configs to generate 0 tokens in the same process when the cache type changes. If you run these scripts yourself and see anomalously low token counts on non-fp16 configs, check that you are not running multiple configs in the same Python session.

git clone https://github.com/rajveer43/veloxquant-mlx
cd veloxquant-mlx
pip install -e .
PYTHONPATH=. python3 benchmark_scripts/run_full_reports.py --models mistral7b falcon3_7b llama31_8b

Figures save to figures/2026-05-12/<model>/. Each model takes 10–15 minutes on M4.

VeloxQuant-MLX is MIT licensed. Hardware: Apple M4, 16GB unified memory. MLX 0.24.x, mlx-lm 0.21.x.

How I built VeloxQuant-MLX — KV cache compression for LLMs running on your Mac​

Every time a language model generates a token, it writes two vectors into memory: a Key and a Value. These accumulate silently in what's called the KV cache — one pair per token, per attention head, per layer.

At a short context that's fine. But at 4,000 tokens with a 7B model on a 16GB Mac, the KV cache alone can consume 4–6 GB. At 32K tokens, it can exceed the model weights themselves.

I wanted to fix this for Apple Silicon. So I spent the last few weeks porting three research algorithms from GPU-targeted papers into a native MLX library — and published it to PyPI as VeloxQuant-MLX.

Here's exactly how it works, what I had to rebuild from scratch, and what the benchmarks actually show.

The Core Idea: Why KV Vectors Can Be Compressed​

Key vectors in a transformer aren't arbitrary. After a learned linear projection, they tend to follow a roughly Gaussian distribution — especially after layer normalization. This matters because scalar quantization theory tells us the optimal codebook for a Gaussian is the Lloyd-Max codebook.

The Lloyd-Max algorithm iterates two conditions until convergence:

1. Optimal boundaries — the boundary between two quantization levels sits at the midpoint of their centroids
2. Optimal centroids — each centroid is the conditional mean of the distribution over its Voronoi cell

c_i = ∫[b_{i-1} to b_i] x·f(x)dx  /  ∫[b_{i-1} to b_i] f(x)dx

For a standard Gaussian, this converges to fixed centroids that you can precompute once and reuse for every layer, every model, every token. No per-block scale constants. No calibration data. Just a lookup table.

The problem: raw KV vectors are not standard Gaussian. They have varying magnitudes, correlated dimensions, and outlier channels that dominate the quantization error.

This is what TurboQuant solves.

Stage 1: Random Rotation​

Before quantizing, multiply each Key vector by a random orthogonal matrix Π:

k̃ = k · Πᵀ

Why? Because a random rotation spreads information uniformly across all dimensions. Outlier channels — dimensions with unusually high variance — get diluted into all other dimensions. After rotation, the vector looks much more like an isotropic Gaussian, and the Lloyd-Max codebook fits it well.

The original TurboQuant paper uses QR decomposition on a random Gaussian matrix to generate Π. That's O(d²) per rotation.

For VeloxQuant-MLX, I added a second option: the randomized Hadamard transform:

k̃ = D · diag(r) · WHT(k)

where D is a fixed scaling factor, r is a random Rademacher vector (±1), and WHT is the Walsh-Hadamard Transform. This runs in O(d log d) and maps directly to mx.hadamard_transform — Metal-accelerated on Apple Silicon.

class HadamardPreconditioner(Preconditioner):
    def apply(self, x: mx.array) -> mx.array:
        # x shape: (..., d)
        scaled = x * self._diag_scale  # diagonal randomization
        return mx.hadamard_transform(scaled) / math.sqrt(self._d)

For head dimensions that are powers of 2 (128, 256, 512) — which covers nearly every production model — the Hadamard preconditioner is automatically selected.

Stage 2: Lloyd-Max Scalar Quantization​

After rotation, normalize each vector to unit norm (store the norm as fp16 for later), then quantize each dimension independently using the precomputed codebook:

for each dimension j in rotated key:
    find nearest centroid in codebook
    store index (b-1 bits)

The codebook has 2^(b-1) centroids for a b-bit configuration. At b=4, that's 8 centroids — enough to achieve ~0.95 cosine similarity with the original key vector after dequantization.

The lloyd_max() function in the library solves this numerically using trapezoidal quadrature:

def lloyd_max(pdf_fn, support, n_levels, n_iter=100, tol=1e-8):
    centroids = np.linspace(lo, hi, n_levels)
    for _ in range(n_iter):
        boundaries = midpoints(centroids)
        centroids = conditional_means(pdf_fn, boundaries)  # numerical integration
        if converged: break
    return centroids, boundaries

You run this once at startup. The result is a tiny lookup table (~8 floats for 3-bit) that gets reused for every token in every layer for the entire generation.

Stage 3: QJL Residual Correction​

Even at 4-bit, MSE quantization leaves a residual error:

r = k - k̂_MSE

TurboQuant's key insight is that you can correct for this without storing the full residual. Instead, store only the sign sketch: apply a random Johnson-Lindenstrauss projection matrix S to the residual, then store only the signs:

z = sign(S·r) ∈ {-1, +1}^m

One bit per sketch dimension. For m=128 sketch dimensions, that's 128 bits = 16 bytes per key vector.

At attention time, the corrected inner product estimate becomes:

⟨q,k⟩ ≈ ⟨q,k̂_MSE⟩ + ‖r‖ · (π/2m) · Σ_j z_j · sign(⟨S_j, q⟩)

The residual norm ‖r‖ is stored as fp16 (2 bytes). The signs are bit-packed (1 bit each). The correction is unbiased — on expectation, it perfectly reconstructs the true inner product.

This is what makes TurboQuant different from plain quantization: the QJL stage recovers information that was thrown away, using only 1 bit per sketch dimension.

Memory Layout: What Actually Gets Stored​

For each token, per attention head, VeloxQuant-MLX stores:

Compare to fp16: 128 dimensions × 2 bytes = 256 B for keys alone.

At 4-bit, key compression is 4.27×. Including values, total KV compression is ~1.6×. With value compression (int8 per-token, already implemented), total KV compression reaches ~3.5–4×.

The Bit-Packing Problem​

MLX has no sub-byte dtype. A 3-bit index stored naively in uint8 wastes 5 bits and kills your compression ratio.

The solution is a BitPackBuffer — pack multiple indices into each byte:

3-bit example: pack [2, 5, 1, 7, 3, ...] into bytes
  byte 0: bits 0-2 = index[0], bits 3-5 = index[1], bits 6-7 = index[2][0:1]
  byte 1: bit 0 = index[2][2], bits 1-3 = index[3], ...

For unpack at attend time, the library uses vectorized numpy paths for b ∈ {1, 2, 4} and a loop fallback for b=3:

if b == 2:
    shifts = np.array([0, 2, 4, 6], dtype=np.uint8)
    vals = ((packed[:, :, None] >> shifts) & 0x3).reshape(n, -1)
    return vals[:, :d]

This runs entirely in numpy before handing off to MLX for the attention computation — keeping the Metal graph clean.

Outlier Two-Stream Cache​

Some attention heads have a handful of dimensions with dramatically higher variance than the others. These outlier channels dominate quantization error: if dimension 47 has 10× the typical variance, every other dimension's codebook entry gets contaminated.

VeloxQuant-MLX handles this with an optional two-stream cache:

1. Calibration phase — observe the first N tokens, track per-channel variance
2. Detection — identify the top-K highest-variance channels
3. Split encoding — outlier channels → int8 with per-token scale; inlier channels → TurboQuant as normal

# During append:
k[:, outlier_idx] = 0           # zero out outliers in inlier stream
encode inlier key with TurboQuant
store outlier channels as int8

# During attend:
scores = turboquant_ip(q, encoded_keys)
scores += int8_ip(q[outlier_idx], outlier_cache) * outlier_scales

The result in benchmarks: at K=8 outlier channels, output quality is indistinguishable from fp16 even at 4-bit compression. The cost is slightly higher memory usage (8 × 2 = 16 extra bytes per token per head) and a numpy↔MLX copy per token — which is the main throughput bottleneck in the current implementation.

Weight Quantization: Compressing the Model Too​

One thing missing from the original papers: they only quantize the KV cache. The model weights stay fp16.

VeloxQuant-MLX adds a QuantizedLinear layer — a drop-in nn.Module replacement that applies TurboQuant to the weight matrix itself:

from mlx_kv_quant.weight import quantize_model

model = load_model(...)
quantize_model(model, bits=4, seed=42)
# All nn.Linear layers are now QuantizedLinear

Each row of the weight matrix is normalized, rotated with the same Hadamard preconditioner, and Lloyd-Max quantized. At inference, dequantize on the fly. At 3-bit, this compresses weight matrices by ~5× with minimal accuracy loss.

Benchmark Results Across 7 Models​

I ran the full benchmark suite (fp16, 2-bit, 3-bit, 4-bit) across 7 models on an Apple M4 MacBook (16GB unified memory):

Key finding: 4-bit is near-lossless on 5 of 7 models. 3-bit is near-lossless on 4 of 7. 2-bit (11.6× compression) breaks generation on all models tested — which matches the theoretical SNR floor (~4 dB at 2-bit vs ~10 dB at 4-bit).

The Qwen3 family underperforms because its thinking-mode models generate a <think> token stream that terminates early when attention quality degrades — a more sensitive indicator than output text quality.

What I Had to Rebuild for Apple Silicon​

The TurboQuant paper targets NVIDIA GPUs with CUDA kernels. Porting to MLX required rebuilding several components from scratch:

1. No in-place mutation. MLX uses lazy evaluation — array slices return views, not copies, and array[i] = value silently fails. Every cache write goes through numpy (pre-allocate numpy arrays, write indices into numpy, hand to MLX only for compute).

2. No sub-byte dtypes. The BitPackBuffer class handles arbitrary bit-widths in pure numpy, with vectorized unpack for b ∈ {1,2,4} and a loop path for b=3.

3. Metal-accelerated Hadamard. MLX exposes mx.hadamard_transform natively. Using it instead of the paper's QR rotation reduces rotation cost from O(d²) to O(d log d) — the entire operation runs on the GPU die of the M-series chip.

4. No monkey-patching. The original turboquant-mlx research code patched mlx_lm.scaled_dot_product_attention() globally. VeloxQuant-MLX exposes a standalone cache.attend(q) method — no global side effects, clean integration with any framework.

5. Pluggable architecture. The production library uses a Factory + Strategy + Registry pattern so you can swap quantizers at runtime without changing model code:

cache = (
    KVCacheBuilder()
    .with_method("turboquant_prod")   # swap to "polar", "qjl", "turboquant_mse"
    .with_head_dim(128)
    .with_bit_width(inlier=4)
    .with_jl_dim(128)
    .build()
)

Three Algorithms, One Interface​

VeloxQuant-MLX implements three quantization algorithms behind the same KVCache interface:

TurboQuantProd — Rotation + Lloyd-Max (b-1 bits) + QJL residual (1 bit). Best quality-per-bit for production use.

TurboQuantMSE — Rotation + Lloyd-Max (b bits), no residual correction. Lower memory overhead, slightly lower quality.

PolarQuantizer — Recursive polar coordinate decomposition. Represents each vector as a sequence of angles at 4 levels, plus a final radius. Geometrically motivated — useful for very low-bit regimes.

QJLQuantizer — Pure 1-bit: sign sketch only. Extreme compression (32× vs fp16), loses most content but preserves attention topology. Useful for very long contexts where quality is secondary to fitting in RAM.

Installing and Using It​

pip install VeloxQuant-MLX

Requires Python ≥ 3.11 and an Apple Silicon Mac.

from mlx_kv_quant import KVCacheBuilder
import mlx.core as mx
import numpy as np

cache = (
    KVCacheBuilder()
    .with_method("turboquant_prod")
    .with_head_dim(128)
    .with_bit_width(inlier=4)
    .with_jl_dim(128)
    .with_seed(42)
    .build()
)

rng = np.random.default_rng(0)
for _ in range(1000):
    k = mx.array(rng.standard_normal(128).astype(np.float16))
    v = mx.array(rng.standard_normal(128).astype(np.float16))
    cache.append(k, v)

q = mx.array(rng.standard_normal(128).astype(np.float16))
output = cache.attend(q)
print(f"Memory: {cache.memory_bytes() / 1024:.1f} KB for {len(cache)} tokens")
# Memory: 193.0 KB for 1000 tokens  (vs 500.0 KB fp16)

What's Next​

The main bottleneck right now is the Python-level encode/decode loop. Every token requires a numpy↔MLX transfer per attention head per layer — that's n_layers × n_heads small copies per generation step. A fused Metal kernel would reduce this to near-zero overhead.

Other items on the roadmap:

• Perplexity benchmark on WikiText-2 for quantitative quality measurement
• Value compression — int8 per-token is implemented, needs tuning at longer contexts
• Longer context evaluation — 8K, 32K token sequences where KV memory dominates
• Fused attention kernel — integrate BitPackBuffer unpack + Lloyd-Max decode + attention dot product into a single Metal dispatch

References​

• TurboQuant (ICLR 2026) — Zandieh et al., "Online Vector Quantization with Near-optimal Distortion Rate"
• PolarQuant (AISTATS 2026) — "PolarQuant: Quantizing KV Caches with Polar Transformation"
• QJL (2024) — Zandieh et al., "QJL: 1-Bit Quantized JL Transform for KV Cache Quantization"
• Apple MLX
• VeloxQuant-MLX on PyPI

TL;DR: The KV cache is the last major unoptimised bottleneck for local LLM inference on Apple Silicon. llama.cpp, Ollama, and LM Studio don't compress it. I built VeloxQuant-MLX to fix that — 9 quantization algorithms, custom Metal GPU kernels, up to 16× compression, plug into mlx_lm with 3 lines.

The wall every Mac LLM user hits​

You've got a MacBook with an M-series chip. You've downloaded llama.cpp, or Ollama, or LM Studio. You load a 7B model, start a conversation, and for the first few hundred tokens everything feels fast.

Then you push it. A long document. A complex coding task. A multi-turn conversation that's been going for a while. And suddenly — generation slows to a crawl, the fans spin up, or the process just dies.

This isn't a bug in those tools. It's a fundamental memory problem that none of them solve. And it lives in a part of the inference stack that almost nobody talks about: the KV cache.

What the KV cache actually is​

To understand the problem, you need to understand what happens inside a transformer when it generates text.

Every token in your context window requires the model to compute two matrices at every layer — a key and a value. These represent what that token "means" in context. Without caching, generating each new token would require recomputing these for every prior token — quadratic cost that makes long-context generation completely impractical.

So instead, the model stores them. Every time a token is processed, its key and value matrices are written to a cache. Future tokens look them up instead of recomputing them.

This is the KV cache. And it grows with every token you generate.

The math is simple:

memory = num_layers × num_kv_heads × head_dim × seq_len × 2 (K+V) × 2 bytes (fp16)

For Llama-3.1-8B at 8k context: 4.2 GB. At 32k context: 16.8 GB. On a machine with 16 GB of total unified memory.

Why Apple Silicon makes this worse, not better​

On a discrete GPU setup — say, an NVIDIA card with 24 GB VRAM — the GPU has its own dedicated memory pool. The KV cache lives there, separate from your system RAM.

Apple Silicon doesn't work that way. M-series chips use unified memory: the CPU, GPU, and Neural Engine all share the same physical memory pool. Your model weights, your KV cache, your browser tabs, your IDE, your OS — all competing for the same 16 or 24 GB.

A 7B model at 4-bit quantisation takes roughly 4–5 GB. Add OS and background processes (~4 GB), Chrome with a few tabs (~2 GB), your IDE (~1 GB). You're at 11–12 GB before you've generated a single token. On a 16 GB machine, you have 4 GB left for the KV cache — which runs out around 6k context for a 7B model.

On a 24 GB machine it's better, but not solved. During the development of VeloxQuant-MLX, I ran a benchmark sweep across 8 models. Qwen2.5-32B failed every quantization config because the weights alone (~17.5 GB) plus OS and development tools left negative headroom for activations and cache buffers. The memory watchdog had to kill the process before it triggered a kernel panic:

[07:28:09] free=62MB   inactive=1094MB pressure=unknown
[07:28:19] free=63MB   inactive=891MB  pressure=unknown
[07:28:19] CRITICAL: only 954MB available — killing PID 21438

System recovered to 10.2 GB free immediately after the kill, with no GPU stall.

What existing tools do about this: essentially nothing​

This is the part that surprised me most when I started digging.

llama.cpp has done extraordinary work on attention kernel optimisation, weight quantisation, batching, and speculative decoding. But the KV cache is stored at fp16 by default. There is experimental support (--cache-type-k q8_0) but it's not the default, not Metal-optimised, and the quantisation methods are basic scalar quantisation with no adaptation to actual key distributions.

Ollama builds on top of llama.cpp and inherits all of this. The UX is excellent. The KV cache situation is unchanged.

LM Studio adds a polished GUI and nice model management. Same story on the cache.

mlx_lm — Apple's own MLX-based inference library — is the most natural fit for Apple Silicon and does excellent work. But its default KV cache is full fp16. No built-in compression.

All of these tools have optimised everything around the KV cache. None of them have solved the KV cache itself.

The insight: compress the cache, not just the weights​

Most quantisation work in the LLM ecosystem targets weight quantisation — compressing model parameters from fp16 down to 8-bit, 4-bit, or lower. This is what GGUF, GPTQ, AWQ, and most Ollama models use.

Weight quantisation is a one-time operation. You compress once, save, load the compressed version.

KV cache quantisation is different. It happens at inference time, on every token, for every layer. The keys and values are different every generation — you can't pre-compute anything.

The key insight is that while weight distributions are relatively uniform, KV cache distributions are not. Keys tend to follow Gaussian or Laplacian distributions after a rotation transform. This structure can be exploited with much more aggressive compression than weight quantisation — down to 1 bit per dimension with surprisingly low quality loss.

The reason this works at such extreme bit rates: the attention computation only needs an approximation of the inner product q · k. Small errors in the reconstructed key vector translate to small errors in the attention score, which average out across many keys in the softmax.

What I built: VeloxQuant-MLX​

VeloxQuant-MLX is a KV cache compression library for Apple Silicon, built on top of MLX. It implements nine quantisation algorithms — from zero-calibration 1-bit methods to mixed-precision allocators — all backed by custom Metal GPU kernels compiled at runtime.

The algorithms​

TurboQuant RVQ — the recommended default​

Zero calibration. Works on any model immediately. Uses Residual Vector Quantisation with analytical Gaussian and Laplacian codebooks precomputed from distribution theory.

Two residual passes at 1 bit each → 7.5× compression with cosine similarity above 0.97.

import mlx_lm
from veloxquant_mlx import KVCacheBuilder, KVCacheConfig

model, tokenizer = mlx_lm.load("mlx-community/Llama-3.2-3B-Instruct-4bit")

config = KVCacheConfig(method="turboquant_rvq", bit_width_inlier=1, seed=42)
caches = KVCacheBuilder.for_model(model, config)
model.make_cache = lambda *_a, **_k: caches

response = mlx_lm.generate(model, tokenizer,
    prompt="Write a 3000-word analysis of the transformer architecture.",
    max_tokens=3000,
)

VecInfer — 16× with Metal acceleration​

Trades a 2-minute calibration step for 16× compression via Product Vector Quantisation. Per-channel smooth scaling handles outlier dimensions that defeat standard VQ. The hot path runs through a Metal GPU kernel that is 13× faster than equivalent MLX Python ops.

Standout result: Qwen2.5-7B VecInfer-1bit exceeds fp16 throughput at 16× compression, likely due to its strong GQA ratio reducing the number of KV heads.

RateQuant — best accuracy per bit​

Runs a 90-second sensitivity probe across all transformer layers, learns which layers are most sensitive to quantisation noise, then uses reverse-waterfilling to allocate more bits to sensitive layers and fewer to insensitive ones.

At 2.0 average bits, RateQuant achieves 2.7× lower perplexity degradation than uniform 2-bit quantisation at identical memory cost.

SpectralQuant — best quality at long context​

Keys in transformer models concentrate ~96% of their variance in just 3–4% of dimensions. SpectralQuant rotates keys into their eigenvector basis via SVD, then applies separate codebooks to the "signal" dimensions (high variance) and "noise" dimensions (low variance).

At 16k context the rotation trick matters enormously — quantisation errors accumulate over long sequences, and aligning the codec to the actual variance structure cuts that accumulation dramatically.

RaBitQ — maximum context length​

1-bit key compression via randomised Hadamard transform + binary sign packing + IVF clustering. Pairs with 4-bit MSE scalar quantisation on values for 6× full KV compression.

6× more context in the same RAM budget.

CommVQ — RoPE-compatible exact VQ (ICML 2025)​

Standard VQ breaks with RoPE positional encodings because quantize(rotate(x)) ≠ rotate(quantize(x)). CommVQ (Apple ML Research, ICML 2025) trains codebooks on pre-RoPE keys with a commutativity projection in the EM M-step. RoPE is applied exactly at decode time. 64× key compression with exact positional encoding.

The Metal kernels​

All the compression math in the world doesn't help if the quantisation itself is slow. VeloxQuant-MLX compiles nine Metal GPU kernels at runtime using mx.fast.metal_kernel:

The fused SDPA kernel is the most impactful. Without fusion, dequantisation creates a full fp16 key matrix in memory before attention — which defeats much of the point of compression. The fused path keeps the cache compressed all the way until attention scores are computed.

The 30-line Metal kernel that powers VecInfer's 13× speedup:

// One thread per sub-vector. Argmin lives in registers — no diff tensor.
uint vec_idx = thread_position_in_grid.x;
float best_dist = INFINITY;
uint  best_idx  = 0;

for (uint c = 0; c < n_centroids; ++c) {
    float dist = 0.0f;
    for (uint i = 0; i < sub_dim; ++i) {
        float d = float(x[x_base + i]) - float(codebook[cb_base + i]);
        dist += d * d;
    }
    if (dist < best_dist) { best_dist = dist; best_idx = c; }
}
out[vec_idx] = best_idx;

The key insight: the [N, n_centroids, sub_dim] diff tensor is never materialised. The argmin accumulator lives entirely in thread-local registers — 98% peak memory reduction at long context shapes.

Real numbers across 10 models​

End-to-end mlx_lm.generate, 200-token prompt, 120-token generation, Apple M-series:

RVQ-1bit is the safe default — within 5% of fp16 on most 7–8B models with zero calibration. VecInfer-1bit wins on compression (always 16×) and throughput on models with strong Grouped Query Attention ratios.

What this means in practice​

On a 16 GB MacBook M3:

• Before: 7B model maxes out around 6k context before generation slows
• After with RVQ 1-bit: that same cache now fits in ~450 MB, leaving headroom for 50k+ token contexts

On a 24 GB MacBook M3 Pro:

• Before: 13B models are borderline; long conversations risk OOM
• After: 13B at 32k context fits comfortably; the cache that would have consumed 8 GB uses 500 MB

Quick decision guide​

Try it​

pip install VeloxQuant-MLX

Requires macOS on Apple M1 or later · Python 3.11+ · MLX ≥ 0.18

📖 Documentation: https://veloxquant-mlx.netlify.app/docs/ 📦 PyPI: https://pypi.org/project/VeloxQuant-MLX/ ⭐ GitHub: https://github.com/rajveer43/turboquant_mac_implementation

What's next​

• Vision-language model support — visual tokens have fundamentally different KV distributions; heavy outliers require adapted algorithms
• Per-head RateQuant granularity — the paper allocates bits per layer-head group; current implementation is per-layer, leaving ~30% accuracy improvement on the table
• Gradient-based sensitivity for RateQuant — more accurate than activation-based, at the cost of a slightly longer calibration pass

The KV cache is the last major unoptimised bottleneck for local LLM inference on Apple Silicon. llama.cpp, Ollama, and LM Studio have built excellent tools — but none of them have solved this. VeloxQuant-MLX is my attempt.

If you're running models locally on a Mac and hitting memory walls — at any context length, on any model — I'd genuinely like to hear about it.

MIT License · Built for Apple Silicon · Engineered for speed