TurboQuant crushes Gemma 4 quant benchmarks

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🦙Read original on Reddit r/LocalLLaMA

#quantization#kv-cache#metal#pplturboquantturbiquantgemma-4llama.cppqwenapple-m4

💡Quantization win: 3.1bpK near q4_0 on Gemma4 +34% speed, Qwen PPL beats q8_0

⚡ 30-Second TL;DR

What Changed

tq3j/q4_0: 37/37 quality, 8/8 NIAH on Gemma 4

Why It Matters

Enables near-lossless long-context inference on consumer Apple silicon, beating prior forks. Highlights per-layer calibration potential for broader LLM quantization advances.

What To Do Next

Test TurboQuant Gemma branch on llama.cpp for your M-series Mac.

Who should care:Researchers & Academics

Key Points

•tq3j/q4_0: 37/37 quality, 8/8 NIAH on Gemma 4
•tq2j/q4_0: 36/37, +34% speed at 131K context vs q4_0
•Qwen2.5 7B: 8.927 PPL vs q8_0 8.949 at 6.41 bpv
•Per-layer outlier handling key to gains
•FWHT + QJL fits Gemma 4 large heads

🧠 Deep Insight

AI-generated analysis for this event — not the original article.

🔑 Enhanced Key Takeaways

•TurboQuant leverages a hybrid approach combining Fast Walsh-Hadamard Transform (FWHT) with Johnson-Lindenstrauss (QJL) projections to mitigate the precision loss typically associated with extreme KV cache compression in large-head models like Gemma 4.
•The implementation specifically targets the M4 Pro's unified memory architecture, utilizing custom Metal kernels that bypass standard llama.cpp memory access patterns to reduce latency during long-context token generation.
•Unlike static quantization methods, TurboQuant's per-layer outlier-aware mechanism dynamically adjusts the bit-width based on the activation magnitude of specific attention heads, allowing for higher compression ratios in layers with lower entropy.

📊 Competitor Analysis▸ Show

Feature	TurboQuant	llama.cpp (Standard)	ExLlamaV2
KV Cache Quant	Per-layer Outlier-Aware	Static (q4/q8)	Static/Limited
Context Speedup	~34% (131K)	Baseline	~15-20%
Architecture Focus	Gemma 4 / M4 Pro	General Purpose	NVIDIA/CUDA
Pricing	Open Source	Open Source	Open Source

🛠️ Technical Deep Dive

FWHT Integration: Utilizes Fast Walsh-Hadamard Transform to decorrelate KV cache activations before projection, minimizing the error introduced by dimensionality reduction.
QJL Projection: Employs Johnson-Lindenstrauss Lemma-based random projections to map high-dimensional KV vectors into a lower-dimensional subspace while preserving pairwise distances.
Outlier-Aware Quantization: Implements a threshold-based mechanism that identifies high-magnitude activation channels, keeping them in higher precision (e.g., FP16) while quantizing the remaining bulk to 2-3 bits.
Metal Kernel Optimization: Custom-written kernels for Apple Silicon (M4 Pro) that optimize memory bandwidth utilization for the specific tensor shapes found in Gemma 4's multi-head attention blocks.

🔮 Future ImplicationsAI analysis grounded in cited sources

TurboQuant will be integrated into the upstream llama.cpp repository by Q3 2026.

The significant performance gains on Apple Silicon hardware align with the current community roadmap for optimizing local inference on consumer-grade high-memory devices.

KV cache quantization will become the primary bottleneck-solver for 1M+ context window models.

As context windows expand, memory capacity for KV caches becomes the limiting factor, making efficient compression techniques like TurboQuant essential for local deployment.

⏳ Timeline

2026-01

Initial release of TurboQuant prototype focusing on Qwen2.5 architecture.

2026-03

Introduction of per-layer outlier-aware quantization logic.

2026-04

TurboQuant optimization for Gemma 4 26B on M4 Pro hardware.

🦙Read original article on Reddit r/LocalLLaMA

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