日本語のみで絞り込む
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 ...
Llama.cpp selects the most efficient kernels at runtime based on detected CPU capabilities. On CPUs that support SME, SME microkernels are enabled automatically ...
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 ...
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 ...
TurboQuant has been rejected by gg. It does not meaningfully outperform llama.cpp's current implementation which uses Hadamard rotations, while being ...
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 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 ...
2026/4/15 -If your current setup is llama.cpp-based (for example, running GGUF models with llama-server or Ollama), the TheTom/turboquant_plus fork adds -- ...