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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 ...
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 ...
Cumulative release since tqp-v0.3.0. This is a large one, with major KV streaming, MoE paging, backend performance, and model support work.
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 ...
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, ...
The new auto head_dim mapping works for 5 of 7 architectures tested (Qwen, Gemma, Mistral, Llama, GLM). Two models fail auto-detection: Phi-4-reasoning-heretic.
The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp : Use the GGUF-my-repo space ...
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 ...
2026/4/19 -A single page for tracking TurboQuant support in llama.cpp — the upstream issue, the open PR, the new tbq3_0 / tbq4_0 KV cache types, community ...
2026/4/15 -Before diving in, one important distinction: TurboQuant does not quantize model weights. It compresses the KV cache at inference time.