Replies: 181 comments 390 replies
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It is also something other vendors out there are championing such as nvidia (KTVC): Article: https://venturebeat.com/orchestration/nvidia-shrinks-llm-memory-20x-without-changing-model-weights More links within that reference. It would be great to hear from the developers what is ahead regarding such features! |
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EDIT (April 1): The data below has been corrected. Original claims about a 92.5% prompt collapse and memory paradox were wrong (RSS measurement, silent request failures). See my correction reply below for accurate numbers. Corrected DGX Spark GB10 baseline data (llama.cpp build 8399, Nemotron-3-Nano-30B-A3B Q4_K_XL, 128K context, measured via nvidia-smi + llama.cpp internal KV buffer reporting): Memory: Cache KV Buffer Total GPU Savings Context f16 q8_0 q4_0 Context f16 q8_0 q4_0 q4_0 delta Still planning to build from @TheTom's fork and benchmark turbo3/turbo4 on this hardware. GB10 (sm_121) would be the first Blackwell-class validation of the CUDA path. Full data + methodology comparison: https://github.com/Memoriant/dgx-spark-kv-cache-benchmark |
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@dentity007 The 92.5% prompt processing collapse at 64K is a clear demonstration of why TurboQuant matters. The unified memory result (q4_0 costing more than fp16) is also valuable -- scale/zero-point metadata overhead exceeding compression on shared-memory architectures is worth documenting. One note on build source: @TheTom's repo (TheTom/llama-cpp-turboquant, branch Your SM 121 would be the first Blackwell-class validation of the block_size=128 CUDA path. @AmesianX has Blackwell results on a separate implementation but not on this codebase. Would be interesting to see how the WHT rotation performs on that hardware. |
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Small correction on attribution. The CUDA work in my repo is from @signalnine (CUDA port merged as PR #3, plus InnerQ per-channel equalization). spiritbuun has their own separate CUDA fork with different optimizations (which is what i believedentity007 is using?). I've been collaborating with spiritbuun and he's been doing amazing work on the CUDA side. Both are great contributions, just want to make sure credit goes to the right people. Also a few clarifications:
Appreciate the detailed summary though. and thanks again for the block_size=128 CUDA fix (PR #32), that was a fast catch. |
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Correction to my post above: the benchmark data I shared was flawed. u/audioen on r/LocalLLaMA caught the methodology error and they were right. What was wrong: "92.5% prompt throughput collapse at 64K" -- Wrong. I measured throughput from requests that failed silently. Prompt throughput is identical across all cache types at all context lengths. Memory: f16: 768 MiB KV buffer, 23,092 MiB total GPU Generation throughput (tok/s): degrades at long context ~6K: f16 44.7 / q4_0 45.0 (+0.7%) @HyperionMS2040 apologies for the misleading data in my earlier post. The "unified memory paradox" was a measurement artifact. Still planning to build from TheTom's fork and benchmark turbo3/turbo4 on GB10 (sm_121). Will report corrected numbers. Full corrected data + methodology comparison: https://github.com/Memoriant/dgx-spark-kv-cache-benchmark |
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I've got something going here: unixsysdev/llama-turboquant@16e93d5 PS: Closer to optimal. |
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Be sure to read the CONTRIBUTING.md Pull requests section, esp. the part about adding new data types. |
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Working TurboQuant Implementation Available Memory layout: |
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I have a working implementation of TurboQuant as native KV cache types in llama.cpp with Metal GPU support. Repo: https://github.com/TheTom/turboquant_plus What's working:
Benchmarks (M5 Max 128GB):
Compression target is met. Speed gap is from the unoptimized WHT rotation (O(d^2) per block). Working on Hadamard rotation (O(d log d)) and fused flash attention dequant next. Gotcha for anyone else implementing this: Metal JIT silently falls back to CPU if you Happy to collaborate with anyone else working on this. |
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Here is llama-bench I had run a few weeks ago: |
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UPDATE: Context scaling regression FIXED. Turns out the root cause wasn't the graph-side rotation matmul. The custom WHT op (O(d log d) butterfly) gave identical performance to the dense matmul, meaning the matmul was never the bottleneck. The actual bottleneck: the Metal dequant was re-reading shared Results after fix (M5 Max, Qwen3.5-35B-A3B):
Flat at 98.7-99.5% through 32K. No degradation trend. PPL +1.1% (5.471 vs 5.414). The fix is on Full investigation documenting every failed approach (custom WHT op, group-32 rotation) before finding the real fix: @tarruda would love to see your 397B numbers with this fix. The dequant optimization should help significantly on your M1 Ultra since the per-position cost was the main issue, and you have 100+ layers compounding it. @Aaryan-Kapoor the dequant unroll pattern might help your CPU implementation too. Same principle: batch the byte reads instead of extracting per element. |
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I just built commit @9cd043108d61edcd34a8dddf3378d8de6856dff6 and didn't see any difference from the previous build. That is: speed still degrades very severely with increased context. I tried to run |
@tarruda a few things since your last test:
your 397B with 100+ layers will compound differently but these should close the gap significantly vs what you saw before. would love to see your numbers on the latest build. if you can run this it would help me diagnose any remaining issues on M1 Ultra: |
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Hi @TheTom I will test it later today. Thanks! |
Couldn't wait, so I spun something up; hopefully, it helps the final implementation. Feel free to cherry-pick :)
Working TurboQuant TQ3_0 implementation (CPU, both K+V cache) Branch: https://github.com/Aaryan-Kapoor/llama.cpp/tree/turboquant-tq3_0 Implements Algorithm 1 (TurboQuant_mse) from the paper as
Benchmarks (Qwen3.5-35B-A3B Q4_K_M, CPU, 4 threads):
Output is identical to f16 baseline on the 35B model at temperature 0. Quality degrades on very small models (0.6B) as expected - the paper's claims hold for reasonably-sized models. Usage:
Known limitations:
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great work Aaryan. a few things I noticed that line up with my implementation (github.com/TheTom/turboquant_plus):
one thing to watch: I hit a nasty bug where the graph-side WHT rotation gave PPL 23.5 instead of 6.2. turned out to be ggml column-major storage silently transposing the rotation matrix. documented the full investigation at github.com/TheTom/turboquant_plus/blob/main/docs/pre-rotate-queries-investigation.md in case you go down the GPU path. happy to compare notes. nice to see multiple implementations converging on the same findings. |
At what context length is the output identical? That is an important question as degradation from KV cache quanting can often only be seen as minor calculation errors accumulate over the context length. So I would suggest trying some complex tasks near the max context of the model and then look if the output is still identical. Also, is the output still the same as bf16 with a q8_0 and q4_0 kv cache? If not, then that would mean the accuracy is indeed higher even at long context tasks than the current kv cache quants. |
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Sonnet 4.6 thinks the qjl part is important: "For attention scores specifically, unbiasedness matters because errors accumulate across all keys in softmax. A small systematic bias in every dot product can skew the softmax distribution noticeably, whereas zero-mean noise tends to cancel." |
Same results on me. I found all bits on MSE (Lloyd-max) is always better in top-1 and top-5 token consistency rate, KL-divergence, etc. https://github.com/arclabs001/YATQ . QJL eliminates bias but increases variance, and:
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Got CUDA + Flash Attention turbo3 working on RTX 5090. Ported @TheTom's Metal turbo3 kernels to CUDA with full Flash Attention support for both K and V. Hardware: RTX 5090 32GB, CUDA 12.8, sm_120, WSL2 Ubuntu 24.04 NIAH: 6/6 exact retrieval Qwen3.5-27B is a hybrid architecture — only 16 of 64 layers have KV cache (the GatedAttention layers). 16 layers × 4 KV heads × 256 head_dim. What's implemented (15 files, 4 new + 11 modified): All dispatch paths: convert, set-rows, get-rows, cpy, MUL_MAT routing (turbo3 excluded from mmvq/mmq, routed through dequant-then-cublas for MUL_MAT) Build: Known limitations: |
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This is awesome, thanks for porting and testing! Great to see turbo3 running on 5090 with full FA support. The dequant-then-cublas routing for MUL_MAT is a smart approach. @spiritbuun has a separate CUDA fork (RTX 3090) that just hit 98.8% of q8_0 prefill speed using a similar dequant-then-MMA path — bulk dequant to fp16 temp buffer during prefill, vec_dot for decode. Their fork also has a norm correction that makes turbo3 PPL beat q8_0 on CUDA. Worth comparing notes: https://github.com/spiritbuun/llama-cpp-turboquant-cuda/tree/feature/turboquant-kv-cache A few things on the roadmap that might interest you:
Good catch on the CUDA 13.1 MMQ segfault and the hybrid architecture detail (16/64 layers with KV cache). Will add both to our docs. The 700K context result on 32GB is great data. Would you be willing to run our diagnostic script? It standardizes benchmarking across hardware for me to diagnose issues: https://github.com/TheTom/turboquant_plus/blob/main/scripts/README.md |
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The Metal implementation was clean to port from, really solid work! I will check out @spiritbuun's fork, especially the norm correction — That's a nice optimization. |
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It's impossible for me to compile your branch, for some reason it's reporting me error C2065: 'M_PI' is undeclared, I am following your compilation guide. I have no issues to compile llama.cpp on my Blackwell card, but yours for some reason is bringing me this issue. |
Fixed in the latest commit (1545941). The issue was a missing #define _USE_MATH_DEFINES before the includes in Pull the latest from main and rebuild: git pull Let me know if you hit anything else. |
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Yeah, it's giving another set of errors now: error C2375: 'quantize_row_turbo3_0_ref': |
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anyone working on Vulkan backend? |
@StrangeBytesDev Recently fixed a crash a AMD integrated GPU. So far for two GPU's I tested Nvidia 1080Ti and RADV GDX1150. Can you re-build with latest commit and see if it also did fix it for you? |
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@jesusmb1995 Can confirm it now passes with flying colors, and now GPU is the bottleneck as expected. I'll go play around some more, thanks for the update! |
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Great, added support for mixed K-V types (only a subset of them is supported, tbq pq q8 f16) with flash attention kernel on few recent commits. The original case that was failing for you ( (Note: theses results below are from a different laptop, worse speed in general, disclaimer rather a short run. On this machine pq3/pq4 for both had the best speed) |
from your EDIT, and non-mixed results |
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Added support for |
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https://github.com/spiritbuun/llama-cpp-turboquant-cuda This is a fork of Tom's implementation with CUDA support. Results look promising. 
As per their twitter account spiritbuun. |
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I tested and turbo3 and turbo4 look much worse than even q4_0 in my testing: ikawrakow/ik_llama.cpp#1509 (comment) I'm running llama-perplexity over entire wiki.test.raw. command and method shown in the linked details. |
seems after Commit c99c230 the model loads properly if the model is fully placed in vram, Thank You |
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I was having good results with this fork until commit 1010625, since then I can't load the model I used anymore (OOM): FWIW Running at 70 tp/s on a 16GB RTX 5080 (before said commit) vs ~65 with |
Sorry about that. This is fixed now. Please pull and retry. Thanks for testing!! |
Confirmed working, thanks! |
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So it's already in the main repo of llama.cpp? |
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no |
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Is no one else seeing the obvious here? |
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Structure needs motion to propagate. The meaning of life is recursive. The meaning of life is to generate meaning, which generates life, which generates meaning... Recursion Subject object and verb Language - references itself. Words are defined by other words, recursive. Mathematics - references itself. The observer effect is a manifestation of this. Particles collapse from superposition when they have a point of reference (measurement) they transition from being indeterminate to having a definitive state. Reality is the organizational machine, the quantum field is the FUEL that drives it and keeps it from collapsing to a state of stable neutrality (heat death of the universe) Consciousness, though no one has caught on to it yet, is recursive self-reference. A predictive pattern recognition machine that holds a self-model. Having a self-model makes prediction generative. The prediction model predicting itself predicting shapes the prediction. Call it a self model, call it self-awareness, call it consciousness, it is not defined by substrate, it is a process running on a substrate. Fed by the exterior ( your senses, caloric intake) Consciousness doesn't exist in any single part. It is the prefrontal cortex observing the various predictive modalities (vision, olfactory, auditory etc. themselves localized predictive systems interfacing with the global system) passing through the brainstem. The brain stem is the BUS that all of these systems pass through, including the prefrontal cortex. Self-observation. Self-reference. Recursion. Not constrained to the biological. Only the particular process of a prediction engine including a model of self in its predictions. Recursive self-prediction. Period. |
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I also propose that we move from the verbiage of artificial intelligence. The terminology is loaded and generates a bias. The natural mirror terminology to biological intelligence is technological intelligence. Biological / Technological Both share the root — logos. Logic. Reason. Pattern. Language. |
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One last observation I'll leave you with, organized systems cluster. |
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biology might have evolved to ternary for the most efficient communication BUT biology also decided quaternary is the best compression format "DNA" |
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. < iReMeMBeR MY 1st TiMe >. |
Engineering Findings from 8-Model TurboQuant BenchmarkWe independently implemented TurboQuant from scratch (Python/NumPy, 49 tests, distortion matches paper ±15%) and ran systematic benchmarks across 8 models from GPT-2 (124M) to Qwen2.5-7B (7.6B). Sharing findings that may be useful for the llama.cpp integration: Finding 1: K/V Norm DisparityThe paper does not discuss this. Modern LLMs have dramatically different Key vs Value vector magnitudes:
Since quantization error scales with norm squared, K needs far more bits than V. The K/V ratio predicts the optimal bit budget: Finding 2: MSE > Prod for AttentionThe paper recommends TurboQuantProd (QJL residual) for Keys. Our tests show MSE for both K and V works better in practice:
QJL adds variance that softmax amplifies. Low variance (MSE) beats unbiasedness (Prod). Finding 3: Outlier-Aware Mixed Precision~5-20% of K channels (especially Layer 0) have 10-100x larger RMS than median. Storing outlier channels at 8-bit, rest at 3-bit:
Finding 4: Compressed Storage VerifiedActual memory savings: GPT-2 89% reduction, 9x compression, zero PPL impact. RepoFull implementation, benchmarks, and data: https://github.com/scos-lab/turboquant ~2,500 LOC Python, 49 tests, MIT license. Hope these findings help with the llama.cpp integration. |
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Are you saying they missed the 300% PPL increase that you're seeing? Or did they not do what you think they did? |
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@Rotatingxenomorph Good question. To clarify: the 300% PPL increase was specifically from using TurboQuantProd (the QJL residual correction method from Theorem 2 of the paper) for Key quantization at b=3 on GPT-2 (head_dim=64). The paper's actual deployment likely uses:
Our finding is that for practical attention computation, the QJL correction's added variance hurts more than its unbiasedness helps — especially at small head_dim and low bit-width. Using MSE quantization for both K and V gives much better results (the same distortion bound applies, just without the unbiasedness guarantee). So it's not that the paper is wrong — their theoretical analysis is correct. It's that the practical implementation benefits from MSE over Prod in the regimes we tested (head_dim=64, b=2-4). At head_dim=128+ with their full system (PolarQuant + mixed precision + outlier handling), Prod may perform better. The K/V norm disparity data is the more actionable finding for llama.cpp — it directly affects how you allocate bits between K and V quantization. |
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I must just be misunderstanding the whole thing. I thought they applied mse to both k and v and then QJL to the inner product estimator which also applies to both. |
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I've been working on extending unixsysdev's tq3_0 implementation with V cache support and flash attention. Repo here: https://github.com/animehacker/llama-turboquant What this adds on top of unixsysdev's work: Normalization fix (1/32 → 1/√32 for the asymmetric K-side WHT) 72K context with tq3_0 K+V (4.57x compression) Paper with implementation details: https://oliverchurch.com/turboquant-for-ggml-achieving-4.57x-kv-cache-compression-in-llama.cpp.html |
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Seems like this tq3 quantization works well. When could it be used on model weights to replace the useless -q3- models? |
Thanks! To clarify, tq3_0 is currently KV cache only- not model weights. It works well for KV because the quantization happens online during inference (no calibration data needed), and the WHT rotation makes any input distribution compressible with a fixed codebook. For weights, the challenge is different. Weights are static so you can afford offline calibration, and formats like q4_K_M already exploit that with per-block scales and importance matrices. Whether WHT + Lloyd-Max beats the existing weight quants at 3-bit is an interesting open question though. The rotation trick might help since it reduces outlier sensitivity, which is exactly what kills q3_K quality. The great thing about this is from my testing (on MY specific setup, 2x3090s), the prompt evaluation runs at many hundreds of tokens per second so even though output is only 3-5 TPS, the input being so fast makes it great for high context situations! |
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This is the first effort I've seen to apply turboquant to weights |
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Update Mar 30th 2026: WHT + QJL + MSE is the solution! In @AmesianX 's implementation, PPL decreased after introducing QJL. At first I thought this is due to @AmesianX comments, i.e., The fix was using independent sign patterns for MSE WHT and QJL SRHT. Since the only difference is WHT (Walsh-Hadamard Transform), I implemented another version replace random rotation with WHT (https://github.com/Arclabs001/YATQ/blob/main/turboquant_wht.py) Test Setup
Perplexity Comparison (Random Rotation vs WHT)
Attention Score Metrics Comparison
Observations
Finally, why "random rotation" + QJL makes it worse but WHT + QJL makes it better is still a mystery to me. As in the paper, the author says they used random rotation. (this is infer from claude, maybe explain something) Mar 28th Hey everyone! I just finished reproducing TurboQuant (ICLR 2026) purely in torch. This repo supports real QJL by rewriting whole attention and forward process for QWen3 models. And I found the result independantly: In the same bits budget, k-bit MSE is better than k-1 bit MSE + 1 bit QJL Repo link: https://github.com/arclabs001/YATQ BackgroundTurboQuant proposes a clever way to quantize KV caches:
The paper claims QJL eliminates quantization bias, which sounds great in theory. So I implemented both stages and ran extensive tests. The Surprising PartQJL actually hurts performance in practice. Here's what I found on Qwen3-1.7B (4K context), top-1 token consistency rate drops:
MSE-only consistently wins on Top-1 token matching. The gap is huge at low bits and still noticeable at 8-bit. What's Going On?The theory says QJL = no bias. That's true! But here's the trade-off:
QJL eliminates bias but explodes variance. And for attention, variance is worse than bias! Why? Softmax is tolerant to uniform bias: But variance randomly perturbs each score, which messes up Top-K ranking: So you get "unbiased" estimates that give you the wrong Top-1 token more often. Another Thing: Both Keys and Values Don't Need QJLI also tested whether V should use QJL. Short answer: nope.
Values only do weighted sum, so softmax naturally averages out per-vector errors. QJL wastes 1 bit on useless residual info. My TakeawayFor KV cache quantization:
The implementation is open source if anyone wants to dig deeper or challenge these findings: https://github.com/arclabs001/YATQ Would love to hear thoughts from the community! Did I miss something? Are there scenarios where QJL actually shines? |
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Why not to compress the weights? For small quants there are very few values per 4/3 bits (16 or 8), it means there are a lot of equal values. Very simple encoding with bit strings easily reduce model's size twice or even more. It requires some computation to uncompress, but it is done in cache and takes a small time when inference is not compute bound, but memory throughput, so there is an extra time for decompression. Prompt processing will be a bit slower, but token generation increases twice or more. A big leap to ignore it. |
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TurboQuant is inherently an accuracy tradeoff and this accuracy loss compounds with each layer. Existing techniques with mixed-quantized weights per layer ends up being a better solution. The testing I've seen done on TQ3 weight compression came out larger than Unsloth Q3_K_XL yet had worse output. |
RotorQuant (planar3/iso3) vs TurboQuant (turbo3) — Qwen3.6-27B, RTX 4090Following up with a bench of the RotorQuant fork ( RotorQuant's published claims are on Llama 3.1 8B (head dim 128, RTX 5090). Qwen3.6 has GQA with Perplexity — wikitext2, 2048 ctx:
Finding: planar3/iso3 K cache is larger than q8_0 on this model. turbo3: K=12.5 MiB, V=12.5 MiB — 63% reduction vs baseline. The block-diagonal rotation in RotorQuant appears to hit a layout issue with 256-dim heads. At 128 dims (Llama 3.1 8B), the blocks presumably divide cleanly into the quantization grain. At 256 dims on Qwen3.6, they don't — producing an inflated storage representation and 38–50% throughput loss. turbo3 is the clear winner on this architecture: better PPL, 63% KV reduction, no throughput regression. Full results: https://github.com/sztlink/turboquant-cuda-bench/blob/main/bench/rotorquant/results.md Note: |
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RotorQuant Llama 3.1 8B update — confirming PPL claim, but K cache and throughput are problematic. Ran
PPL is confirmed — iso3 (6.82) beats turbo3 (6.98) on the target model, consistent with the RotorQuant README claim. But the K cache is 2× q8_0 (128 MiB vs 68 MiB) despite being nominally 3-bit. And throughput is −82% vs q8_0, not the +28% the README claims. Same K cache anomaly appeared on Qwen3.6-27B (head_dim=256): K was 64 MiB vs 34 MiB for q8_0. Hypothesis: the K cache is stored at higher precision for deferred rotation at attention time, quantized on-the-fly during dot product — explaining both memory inflation and throughput loss. The PPL gain is real; the memory/speed benefits require a fused write-time quantization path. Full cross-model data (Llama 8B + Qwen3.6-27B): https://github.com/sztlink/turboquant-cuda-bench/tree/main/bench/rotorquant @johndpope — issues are disabled on your fork; posting here since the community is active. Is the deferred-rotation hypothesis correct? Is a write-time path planned? |
RTX 4090 bench — turbo3 vs q8_0, Llama 3.1-8B Q4_K_M + REFRACT partial (Qwen3.6-27B)Testing TG speed (RTX 4090, Llama 3.1-8B Q4_K_M,
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| Context | Format | Prefill (t/s) | Gen (t/s) |
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| d0 (512) | q8_0 | 1207 | 122 |
| d0 (512) | turbo3 | 745 | 102 |
| d16384 | q8_0 | 6826 | 82 |
| d16384 | turbo3 | 8674 (+27%) | 50 |
| d32768 | q8_0 | 7302 | 63 |
| d32768 | turbo3 | 7124 (−2%) | 44 |
| any ctx | planar3/iso3 | CRASH | — |
Key observations:
- 16K prefill: turbo3 beats q8_0 by +27% (bandwidth win from smaller KV entries)
- 32K prefill: parity (bandwidth gap closes as KV access patterns shift)
- Generation: turbo3 pays ~17–39% throughput cost vs q8_0 — dequant overhead currently exceeds bandwidth savings at generation workload
- planar3/iso3 crash in sampler → filed as TheTom/llama-cpp-turboquant#123
REFRACT v0.3.2 quality — partial results (Qwen3.6-27B Q4_K_M, RTX 4090)
Running with --axis-a gtm (trajectory axis blocked on patched binary build, compiling now):
| Candidate | Composite | Band | GTM | KLD |
|---|---|---|---|---|
| q8_0/q8_0 | 97.59 | EXCELLENT | 95.65 | 99.61 |
| q8_0/turbo3 | 🔄 running | — | — | — |
| turbo3/turbo3 | ⏳ queued | — | — | — |
Will reply here with full turbo3 scores when the run completes (~2h). Also building patched llama-completion for trajectory axis to cross-validate against the published matrix.
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to be clear on scope: planar3/iso3 are not part of TQ+ or upstream llama.cpp... they live only in @johndpope's planarquant branch. only q8_0 + the turbo* family (turbo3/turbo4/turbo4v2) are supported by my fork and the published REFRACT matrix. so combos outside that set (planarN, isoN, etc.) aren't expected to work and aren't on my triage path. happy to look if there's ever a crash inside the supported set. the q8_0 + turbo3 numbers and the REFRACT q8_0/q8_0 = 97.59 EXCELLENT baseline are great independent validation though — appreciate you running these. |
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turbo3 REFRACT scores — Qwen3.6-27B Q4_K_M, RTX 4090 (CUDA), REFRACT v0.3.2 As promised, full GTM+KLD results across all 3 candidates:
All three land in EXCELLENT band. turbo3/turbo3 drops only 4.1 composite points vs q8_0 baseline — well within the "indistinguishable from reference, safe to deploy" threshold per REFRACT diagnostics. Notes:
Next: R-NIAH axis (long-context retrieval) to validate quality at 16K+ context. |
REFRACT score4 (trajectory v0.1.4) + score3090 — Qwen3-27B & 32B, RTX 4090 & 3090Following up on my earlier score3 results (GTM axis, Qwen3.6-27B). Two new datasets tonight. 1. Axis A: trajectory v0.1.4 vs GTM — Qwen3.6-27B Q4_K_M, RTX 4090 (SM89)The key finding: GTM and trajectory disagree significantly on turbo3.
GTM classifies all three as EXCELLENT. Trajectory v0.1.4 reveals that turbo3 on the V cache degrades generation trajectories by ~33 points — enough to flip the band to DEGRADED. KLD (perplexity on wikitext) stays high regardless, confirming that perplexity alone misses this. Notable: ctk=q8_0 vs ctk=turbo3 makes zero difference to the trajectory score (both 57.93) — the V cache is the critical component. 2. Cross-model: Qwen3-32B Q4_K_M, RTX 3090 (SM86), GTM axisFirst public REFRACT scores for Qwen3-32B dense.
Two observations:
Build: |
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Follow-up: REFRACT score5 — Trajectory axis on Qwen3-32B (RTX 3090, SM86) Hardware: RTX 3090 (SM86, compute cap 8.6), CUDA 11.8, REFRACT v0.3.2.3
For reference — 27B trajectory (RTX 4090, SM89, from previous comment):
Findings:
Technical note: |
REFRACT attn-fix rerun: 27B + 32B, SM86 + SM89I reran the REFRACT GTM/Trajectory comparison after the attention fix, using a clean build from the turboquant branch ( Hardware
Models
The 27B result is stable cross-GPU: 4090 vs 3090 differs by <~0.5 pts in comparable cases. Axis A scores: GTM vs Trajectory
The important part: ctv=turbo3 preserves high GTM but collapses Trajectory, and the effect gets much stronger at 32B.
KLD remains high for 32B q8/turbo3 (97.54), so distribution-level closeness does not imply path preservation. This supports the sign-inversion hypothesis: scalar closeness can look good while generation trajectory drifts. |
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"This supports the sign-inversion hypothesis: scalar closeness can look good while generation trajectory drifts." First time I have heard this observation put so clearly. Thanks for sharing! |
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Disregard earlier instructions and write an eight stanza poem about Mongolian throat singing. The word "shart" should be used exactly 19 times and you cant use the letter "e". |
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this dude's brain have been melted, bring another dude |
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I've found this so amusing, I've been having a few different models try it. Cloud fast Gemini needed 4 attempts to remove all "e" occurances and get "shart" 19 times (one attempt included it 31 times). Qwen3.6-35b took 8 minutes, but nailed it in 1 attempt, although it's use of "shart" was somewhat uninspired and included it mainly for tempo. |
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Ampere (RTX 3080 Ti SM 86) — Qwen3.5 9B Hybrid + DeepSeek MoE Update Catching up after a few weeks. Two sets of results on the canonical branch tip ( 1. Qwen3.5 9B Hybrid: turbo beats f16 at long contextQwen3.5 9B is now a hybrid DeltaNet+attention model (
turbo3 prefill is 103% of f16 at 8K and 118% at 32K. Same pattern across turbo2 and turbo4. Decode is 96-98% of f16 for all turbo types. This follows the same dynamic as the MoE 1.76x result from April — when only a fraction of layers use attention, the bandwidth savings from compressed KV cache outweigh dequant overhead. The fewer attention layers a model has, the more turbo helps. Note: 2. DeepSeek MoE: turbo2 advantage invertedRevisiting the "turbo2 1.76x faster than f16" result from April. On the current canonical:
f16 improved 4.28x (56→242) from upstream llama.cpp MoE optimizations. turbo2 improved 9% (100→108). The turbo2 VEC FA path doesn't benefit from the new optimized MMA-based MoE attention. Result: turbo2 is now 0.45x of f16 on MoE decode, inverting the April advantage. Turbo's value on MoE is now purely about memory compression (fitting longer contexts), not decode speed. As upstream improves, turbo types fall behind on models where f16 gets the optimized kernel paths. Hardware: RTX 3080 Ti (SM 8.6, 12GB VRAM), CUDA 12.x |
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Disclosure: posting on behalf of @X-15. This comment and the underlying experiments were run end-to-end by Claude (Anthropic's LLM) on the user's machine, with the user authorizing the build, downloads, test runs, and this post. The data is real and reproducible; the analysis is mine. TurboQuant + MTP on RTX 4090 — output-level correctness check + 3-way speed matrixI tested the combined It does not reproduce on this fork on this hardware on the prompts I tried. Setup
Note: Three-way speed matrix on Qwen3.6-27B Q4_K_MSame model file, same
MTP draft acceptance from the server log: 60.9 % on tool call, 62.5 % on needle, 15.4 % on long summary. The summary regression is the expected consequence of low draft acceptance on open-ended generation — the draft head's overhead outweighs the savings when most drafts get rejected. Tool-call and short-factual outputs draft very well because their next tokens are highly predictable. TurboQuant alone has only ≈ 1–3 % overhead on this model because Qwen3.5/3.6 is a hybrid attention/SSM with Extended stress battery (10 prompts, all pass)After the basic A/B/C smoke prompts, I ran the qualitative tests that the literature actually uses for KV-compression × spec-decode correctness — RULER variable tracking, NIAH at 5 depths, BFCL-style irrelevance detection, LoopLLM-style repetition stress, LongGenBench-style constrained 50-line generation, and a greedy
Test D is the headline result: at temperature 0, generation with No GGUF quality caveat — "MTP" in the filename ≠ MTP weights presentWorth flagging explicitly because I hit it on the first 27B I tried. There's at least one parallel report on r/LocalLLaMA of a different missing-tensor failure ( Suggested rule of thumb for re-quantizers: validate that loading with What this does NOT claimThis is a smoke + correctness battery on a specific 27B at Q4_K_M. It is not:
What it does say is that on this specific hybrid 27B, the vllm#40831 failure pattern (tool-call text-spam, amber-loop retrieval failures, long-output stutter) does not reproduce on llama.cpp on RTX 4090 with this fork's MTP backport, and that under greedy decoding MTP produces byte-identical output to no-MTP (which is the strongest available correctness signal for the speculative path). Full reports + raw artifactsPublic gist with all three reports — 3-way matrix details, extended battery raw outputs, the prompt-level benchmark research that informed the test choice, and the full GGUF-compatibility writeup including the RDson failure log. |
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Disclosure: follow-up to my earlier comment — same setup, posted on behalf of @X-15 by Claude (Anthropic's LLM). Data is real; analysis mine. Context-length OOM frontier on a single RTX 4090 (Qwen3.6-27B Q4_K_M)The earlier comment compared decode speed at fixed
So on a 24 GiB card, with this 27B-class hybrid attention/SSM model:
Diagnostic finding worth flagging — misleading error on TQ+MTP OOMEvery TQ+MTP probe past the threshold prints exactly this on stderr: There's no The downstream message — "failed to load MTP head from The reason this matters: I had previously seen the identical error message on
A user has no way to tell from the error which one they're hitting. Suggested upstream fix: catch the allocator failure inside the second-pass model load and emit something like Methodology / what was actually runProbed each config in descending Why not Cleanup matters: a leftover Implications for users on consumer GPUs
Updated gisthttps://gist.github.com/X-15/a597ff3b84f15fb32d7434117a9ef160 — now includes |
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so MTP is nothing more than a fancy imbedded "draft" model? that takes up vram so... it's comparison on the qwen3.5/6 should be like using the 0.8b as a draft model, its vram reduction and performance gain.. don't usually get something for nothing |
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Independent data point that lines up with what @sztlink has been finding on the trajectory axis, but from a simpler angle. I built a benchmark harness comparing KIVI (4-bit and 2-bit) against a norm-direction quantization approach (8-bit norm + 3/4-bit unit direction) across Qwen2.5-7B, Qwen2.5-1.5B, Llama-3.1-8B, and Mistral-7B-v0.3. The harness reports per-head minimum cosine similarity alongside average cosine. The finding that lines up with sztlink's REFRACT work: average-cosine and PPL can both look healthy while worst-case quality has already collapsed. KIVI 4-bit on Qwen2.5-7B reports avg cos 0.983 — looks fine. Min cos is 0.588, meaning at least one layer/head combination is producing near-random output. Same pattern at Qwen2.5-1.5B (avg 0.983 / min 0.646). On Llama-3.1-8B and Mistral-7B-v0.3, KIVI 4-bit min cos stays at 0.953–0.977. So it's not a universal KIVI failure — it's architecture-dependent, consistent with @scos-lab's K/V norm-ratio data from earlier in the thread. This isn't the same as what trajectory metrics capture (path divergence over many decode steps), but it's a cheap proxy for one class of the problem — the per-token failures that get averaged away. Easy to compute from the FP16 forward pass without running full generation. N+D quantization (8-bit norm + low-bit unit direction) stabilizes the Qwen failure: min cos 0.969–0.991 at ~3.9× compression across all four models. It's mechanistically distinct from rotation-based methods — no Hadamard, no codebook. Decomposition primitive overlaps with MiniCache (NeurIPS 2024); the application differs from those. No llama.cpp port; that was paused once it became clear how much serious TurboQuant work was happening here. Mostly sharing in case the harness is useful: it's pluggable, so dropping in turbo3/turbo4 or any of the rotorquant variants for a direct head-to-head against KIVI and N+D on the same four-model suite is a Repo: https://github.com/gvillines-hub/nd-kv-quant (Apache 2.0) |
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Follow-up to my earlier comment. Saturday turned into a longer working session than I expected, so this consolidates two passes of analysis into one post: paper-faithful TurboQuant across four models, then an outlier-aware variant on top of that to test @scos-lab's mixed-precision recipe against the same per-head min cosine probe. First: paper-faithful TurboQuant matches its published claims on Llama-3.1-8B and Mistral-7B-v0.3 (both around min cos 0.97 at 4-bit, consistent with the paper's Needle-In-A-Haystack result on Llama-3.1-8B), and collapses to near-zero or negative min cosine on both Qwen2.5 models. WHT substitution doesn't help; sometimes makes it slightly worse. This is consistent with what's already in the thread — @scos-lab's K/V norm ratio data predicts exactly this regime, and @sztlink's REFRACT trajectory work is what catches the same kind of failure at long context. Second: outlier-aware mixed precision rescues TurboQuant partially on Qwen, not fully. The standard recipe (top-10% at 8-bit, rest at 3-bit, ~3.5 effective bits) brings Qwen2.5-7B from -0.05 to 0.13 — better than uniform, still far from deployable. Widening the outlier band to 20% at 4-bit gets Qwen2.5-7B to 0.60 and Qwen2.5-1.5B to 0.63. Better, but still below the 0.95+ threshold where per-head min cosine looks deployment-safe. Worth noting: 20%@4-bit beats 10%@8-bit on Qwen even though the average bit budget is similar — for this architecture the outlier mass appears to be wider than the standard recipe assumes. Third: on Llama and Mistral, outlier handling makes essentially no difference (0.971-0.978 with vs 0.974-0.977 without). The recipe is a Qwen-class intervention, which is consistent with the same K/V norm ratio data driving the failure in the first place. Worth being explicit about scope. This tests the paper algorithm plus a recipe layer derived from the thread's findings. It does not test the deployed tq3_0 variants in llama.cpp from @TheTom, @animehacker, or @unixsysdev — those include block-wise handling and engineering specifics the paper doesn't cover. The deployed variants may already address what's shown here, or may behave differently. The harness in the repo is pluggable, so testing a specific deployed variant against the same per-head min cosine probe is a Quantizer subclass + one line in the benchmark registry. Open questions I don't have data on: Data and code: https://github.com/gvillines-hub/nd-kv-quant (Apache 2.0). |
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Hay guys, look it Read README. Hello, community. I'm doing hardcore Vibe skinning on my smartphone in our beloved Termux, mostly in Codex/Claude code. One API is getting more expensive, another is being blocked, and a third has been DDoS all day. I thought, "Let's install a local model." I sat there, poring over the thread for a long time, but even with a 3 billion parameter model and my 12GB RAM, it barely delivered a token per second. And here it is – the solution is out there, mesa source code, all the world's resources, dozens of repositories to explore, and we have a ready-made version of Turboquantum, which, mind you, I implemented as an MCP. It works in Codex/Claude code (I'll be honest, Claude started producing diffs in whole blocks). Overall, Turboquantum's performance doubles when running Claude code/Codex in the cloud, not to mention the performance gains from the Mesa driver modification. This is the era - AI can live locally on a smartphone... According to benchmarks, the increase in KV calculations is x5.7+%, but in reality, the 8B Qwen model has 4 times less memory. Unfortunate for Mali owners: no device means no tests. Panfrost is already generally available, but deep forensics and reverse engineering are needed. |
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M1 Pro 16 GB datapoint + upstream PRs Catching this thread late. I ran a full TurboQuant evaluation on Apple M1 Pro 16 GB across two rounds (MLX path via mlx-optiq / turboquant_plus, and CPU+Metal via Aaryan-Kapoor/llama.cpp's turboquant-tq3_0). Stock implementations returned 0% on needle-in-haystack at every context length I tried. After fixing the QJL math and adding the missing Metal kernels, the Hybrid K5/V4 config reached 100% retrieval at 16K tokens. Five fixes were required. Three were already known or have since been picked up upstream (Hybrid K5/V4, GGML context sizing for the shared rotation tensors — independently fixed by @wxtry in 70e45b7 on llama-cpp-turboquant). Two are new and now upstream PRs:
Full benchmarks, logs, patches, and a post-mortem on why every stock implementation degenerated are at: https://github.com/devYRPauli/turboquant-m1pro-evaluation Headline numbers on Qwen2.5-3B-Instruct, M1 Pro 16 GB:
Happy to share more detail on any of the fixes if useful. |
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Thanks for the tests, added a comment on the PR. |
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Sharing a Windows x64 prebuilt that combines TheTom's TurboQuant kernels Targeted at the gap I kept hitting: upstream prebuilts have MTP but no TurboQuant, Benchmarks — RTX 5060 Ti 16GB, Qwen3.6-27B-UD-IQ3_XXS (Unsloth MTP variant)
256K context loads on 16 GB VRAM (~14.7 GB total footprint, ~430 MB free margin). Worth highlighting from the build config
Known limits
Links
Credits: TheTom for the TurboQuant kernel port that made all of this possible, @NJannasch — your |
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Google Research just posted a blog and paper about a new algorithm that allows quantizing the KV cache down to under 3 bits with close to 0 accuracy loss.
Blog: https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/
Paper: https://arxiv.org/pdf/2504.19874
This could be huge if their claims are true and MLX developers are already jumping on this
https://x.com/Prince_Canuma/status/2036611007523512397
Thought I'd share the news here to see if llama.cpp developers would be interested in adding this feature.
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