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test1111111111111112 / llama-cpp-turboquant-gemma4

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TurboQuant llama.cpp fork with optimized turbo4 kernels for Gemma 4 D=256/512 heads — lazy K/V, batch decode, warp-cooperative write. 120 t/s with 3.8x KV compression on RTX 3090.

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Found Apr 14, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

Performance-optimized fork of llama.cpp for running Gemma 4 26B model at high speeds with turbo4 KV compression on consumer GPUs like RTX 3090.

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AI-Generated Review

What is llama-cpp-turboquant-gemma4?

This C++ fork of llama.cpp delivers turbo4 kernels optimized for Gemma 4 models with D=256/512 heads, enabling lazy K/V processing, batch decode, and warp-cooperative writes. It crushes KV compression at 3.8x while hitting 120 t/s on an RTX 3090—matching f16 speeds but slashing VRAM for 256K contexts on 24GB cards. Build with cmake and CUDA, then run llama-server with --cache-type-k turbo4 --flash-attn on for Gemma 4 inference without quality loss.

Why is it gaining traction?

Unlike stock llama.cpp, it eliminates speed penalties from turbo4 quantization on Gemma 4's quirky head dims, fitting massive contexts where f16 OOMs. Devs love the plug-and-play drop-in: same CLI, instant 3.8x KV savings via --cache-type-v turbo4. Batch decode and optimized kernels make it a no-brainer for high-throughput local runs on 3090-class GPUs.

Who should use this?

GPU-bound ML engineers fine-tuning or serving Gemma 4 on consumer NVIDIA hardware like the 3090. Local AI tinkerers pushing 256K contexts for RAG or long-doc QA without cloud costs. C++ inference power users tweaking llama.cpp for production-grade Gemma deployments.

Verdict

Grab it if you're on Gemma 4 and RTX 30/40-series—benchmarks prove the 120 t/s payoff. But with 19 stars and 1.0% credibility, it's an early fork: verify outputs, watch for upstream merges, and contribute tests.

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