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The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in ...

9時間前 -The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally ...

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud ...

The honest answer: TurboQuant is most impactful on mid-range single GPU setups. On dual H200s at short-to-medium context, standard fp16 KV cache is hard to beat ...

2026/3/26 -I wanted to self test the TurboQuant research from google but specifically via llama.cpp. The first image is from Aaryan Kapoor on the PR ...

Cumulative release since tqp-v0.3.0. This is a large one, with major KV streaming, MoE paging, backend performance, and model support work.

TurboQuant has been rejected by gg. It does not meaningfully outperform llama.cpp's current implementation which uses Hadamard rotations, while being ...

LLM inference in C/C++. Contribute to TheTom/llama-cpp-turboquant development by creating an account on GitHub.

This guide lets you run a local LLM server that can handle up to 100 000 tokens of context on a typical desktop GPU. By building the provided Docker image, ...

Built on llama.cpp with the TurboQuant KV compression and ExpertFlow MoE optimization engines. Coexists with Ollama — no need to uninstall Ollama or llama.cpp.