SaaS Metrics
Gemini Executive Synthesis
TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware.
Technical Positioning
Achieving superior LLM inference speed (tokens/sec) through TurboQuant optimizations on Apple Silicon (M1).
SaaS Insight & Market Implications
This issue reports a critical failure in TurboQuant's core value proposition: performance improvement. On Apple M1 hardware, `turbo3` and `turbo4` not only fail to increase `tokens/sec` but actually degrade performance compared to the baseline `llama-cpp`. This directly undermines the market viability of TurboQuant as a speed optimization for Apple Silicon users. For B2B SaaS, performance regressions are unacceptable. Solutions promising efficiency gains must deliver consistently across target hardware. This indicates a significant engineering challenge in optimizing quantization for specific architectures, highlighting the need for rigorous cross-platform benchmarking and targeted development to ensure promised benefits materialize.
Proprietary Technical Taxonomy
Raw Developer Origin & Technical Request
GitHub Issue
Mar 29, 2026
Repo: TheTom/turboquant_plus
No Difference in tokens/sec - Ministral3 8B Q5_K_M
I used the repo to rebuild llama-cpp from scratch to a different dest compared to original llama-cpp. I am comparing performance of the same base model being executed with same command line parameters using llama-server -m for turbo3 and turbo4. Not seeing any improvement in tokens/second before and after. Actually before the speed of generation is better than after. I am using MAC M1 with 32GB RAM.
Developer Debate & Comments
I missed the part where you highlight the fact that tokens/sec may actually degrade with the added compression of KV cache. I tried with turbo3, do not see noticeable degradation but certainly see major impact on memory consumption and the ability to use much bigger context windows. Question I have now, is can you take advantage of this from within lm studio or ollama?
这个只是对kv缓存压缩，所以只是提升了最大推理上下文的大小。对模型量化压缩和推理速度，是没有提升的。 原来10G显存，如果是一个9b模型，最大上下文128k可能就OOM了。现在压缩后，就可以支持128k上下文了。
You can just use it like Ollama or LM studio without the slow development or wrapper overheads. I use llama cpp directly and use it with router mode and many llms configured with models.ini and interface it with Open Web UI you can find others too. It's nice, but it has a lot more overhead in setting it up. Use `llama-server --host 0.0.0.0 --port 8080 --models-preset /path/to/ini/models.ini` and access it through an Open AI compatible UI like Open Web UI at `http://:8080`. Or 127.0.0.1 as the loop-back if not connecting to a different computer as I do. Here is an example model ini file I use for my 5070ti. [models.ini.txt](https://github.com/user-attachments/files/26391236/models.ini.txt)
Adjacent Repository Pain Points
Other highly discussed features and pain points extracted from TheTom/turboquant_plus.
Extracted Positioning
turbo3 quantization for LLM KV cache compression
Achieving 4.6x compression with quality (perplexity, KL divergence, NIAH) comparable to q8_0 (within 2% PPL) and superior to q4_0, while maintaining high inference speed.
Top Replies
## CRITICAL: Perplexity test reveals quality failure | Cache | PPL | vs f16 | |-------|-----|--------| | f16 | 6.121 | baseline | | q8_0 | 6.111 | -0.16% | | q4_0 | 6.142 | +0.34% | | **turbo3** | ...
## Root causes found ### 1. V cache in rotated space Python verification: dequant output has cosine=0.02 with input (garbage). After inverse rotation: cosine=0.987 (correct). V cache values MUST be...
## QUALITY FIXED ✅ Perplexity with inverse rotation restored in dequant: | Cache | PPL | vs q8_0 | |-------|-----|---------| | f16 | 6.121 | — | | q8_0 | 6.111 | baseline | | q4_0 | 6.142 | +0.5% ...
Extracted Positioning
`turbo3` decode performance for LLM inference on Apple Silicon (M1, M2 Pro, M5 Max), specifically addressing the 'decode cliff' at increasing context depths.
Achieving flat, high-performance `turbo3` decode ratios (0.90x+ of `q8_0`) across all context depths on Apple Silicon, minimizing performance degradation from memory access patterns.
Top Replies
## M2 Pro Results: Bit-Arithmetic Dequant **Hardware:** Apple M2 Pro, Apple8 (1008), has_tensor=false, 32GB **Model:** Qwen2.5-7B-Instruct-Q4_K_M **Build:** experiment/m1-m2-decode-comparison (auto...
## M2 Pro Results Update: Batched Extract IS a Win True baseline comparison (same branch chain, same build): | Depth | q8_0 | Main (const LUT) | Batched extract | Bit-arithmetic | |-------|------|-...
## BREAKTHROUGH: Profiling isolation identifies exact bottleneck Added TURBO_PROFILE_MODE (0-4) to strip away dequant layers one at a time. ### M5 Max vs M2 Pro at 8K context decode: | Mode | What ...
Extracted Positioning
TurboQuant (`-ctk turbo3 -ctv turbo3`) integration with Vulkan devices for LLM inference.
Achieving broad hardware compatibility for TurboQuant, specifically extending to Vulkan-enabled AMD GPUs.
Top Replies
turbo3 currently only supports Metal, CUDA, and ROCm/HIP backends. the Vulkan backend doesn't have a SET_ROWS kernel for the turbo3 quant type yet. since you have an RX 7900 XTX, ROCm would be your...
i hope the rocm issues get fixed i'm interested to try this out!
Yeah we really need some more rcom support from the community. i only have so many viable devices to play with at home
Extracted Positioning
TurboQuant's quantization strategy, specifically regarding K/V norm disparity, attention quantization methods (MSE vs. Prod), and outlier detection (dynamic vs. fixed).
Advancing TurboQuant's quantization efficacy to achieve lower perplexity (PPL) and higher compression (lower average bit rates) through refined techniques.
Extracted Positioning
turbo3 and turbo4 quantization implementation, specifically related to block size changes and kernel instantiation.
Ensuring correct and robust implementation of different quantization schemes (turbo3, turbo4) across varying block sizes and head dimensions, preventing data corruption and out-of-bounds access.
Frequently Asked Questions
Market intelligence mapped to TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware..
What problem does TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware. solve?
Based on our AI analysis of the original developer request, its primary technical positioning is: Achieving superior LLM inference speed (tokens/sec) through TurboQuant optimizations on Apple Silicon (M1).
How is the developer community reacting to TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware.?
Yes, we have tracked 3 direct responses and active debates regarding this specific topic originating from GitHub Issue.
What are the foundational technologies related to TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware.?
Our proprietary extraction maps TurboQuant (turbo3 and turbo4) performance optimization for LLM inference, specifically on Apple M1 hardware. to adjacent architectural concepts including tokens/sec, llama-cpp, llama-server, turbo3.
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like turbo3 and llama-server by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.