Tag
llama.cpp
llama.cpp is a local inference stack for running LLMs on CPUs, GPUs, and edge devices with tight memory budgets. The topic often covers quantization, KV cache optimization, cold-start latency, and how it fits into fine-tuning and multimodal workflows.
11 articles
AtomicBot’s llama.cpp fork boosts throughput on two fronts
4 ways AtomicBot’s llama.cpp fork speeds up Gemma 4 and Qwen 3.6, with matrix-bench gains up to 30-50% on the right setup.
llama.cpp vs vLLM: Choosing the right local LLM engine
llama.cpp and vLLM are local LLM inference engines for different hardware and traffic patterns.
Run MiniMax M3 locally in Unsloth Studio
Set up Unsloth Studio to download and run MiniMax M3 on your own machine.
Open-source AI software is winning on infrastructure, not hype
Open-source AI software is winning because it now powers the core infrastructure for building, serving, and shipping models.
llama.cpp’s latest release proves the project still wins by tightenin…
llama.cpp’s latest release shows that careful kernel fixes and backend tuning matter more than flashy features.
Ollama is becoming the default local AI layer
Ollama is no longer just a local model runner; it is turning into the default AI layer for apps and agents.
Gemma 4 12B: Specs, Benchmarks & How to Run It Locally
Gemma 4 12B is a local-first multimodal model you can run on a 16 GB machine.
Why llama.cpp’s release notes matter more than its model bragging
llama.cpp’s latest releases show that backend correctness drives real speed gains.
Why llama.cpp should treat TurboQuant as the new default path
TurboQuant is the right direction for llama.cpp because asymmetric KV compression cuts memory without breaking compatibility.
llama.cpp adds local LLM inference in C/C++
ggml-org’s llama.cpp keeps expanding local LLM support with OpenAI-compatible serving, browser WebGPU, and broad hardware backends.
5 KV cache takeaways for llama.cpp users
5 takeaways from TurboQuant: under-3-bit KV cache compression, memory savings, and the tradeoffs llama.cpp users should watch.