Market Trends
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
No active discussions extracted for this entry yet.
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.
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.
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like turbo3 and turbo4 by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.