There's a growing interest in running large language models locally, leading to optimizations and community support for various models. This trend emphasizes accessibility and control over AI processing.
OpenHands announced that the MiniMaxAI M2.5 model has 230 billion parameters, with 10 billion active parameters. Currently, the model is not yet available on Hugging Face. The news was shared via a Reddit post.
A discussion in the LocalLLaMA community raises questions about the admissibility of content related to models not specifically designed for local execution. The user proposes prioritizing discussions and resources focused on models and tools that su...
Foxconn chairman Young Liu projects strong growth for 2026. The company has also secured an oversubscribed sustainability loan, indicating investor confidence in the Taiwanese electronics giant's long-term strategy.
A Reddit post, accompanied by the hashtag #SaveLocalLLaMA, highlights the importance of supporting and developing large language models (LLMs) that can be run locally. The discussion emphasizes the need for open-source and self-hosted alternatives to...
The llama.cpp library has added support for the Kimi-K2.5 model. This integration allows users to utilize the model directly within llama.cpp, expanding the options available for local language model inference.
MCP (Multi-Control-Panel) support in llama.cpp is now available for testing. This integration introduces new features, including system message management, a CORS proxy server, and advanced tools for prompt and resource management. The goal is to pro...
The LocalLLaMA community has expressed positive opinions about Kimi, a large language model, favorably comparing it to ChatGPT and Claude. Some users consider it superior in certain applications, opening new perspectives for local inference and use i...
A user reported the effectiveness of the Step-3.5-Flash model, highlighting its superior performance compared to larger models like GPT OSS 120B in certain contexts. Its availability on OpenRouter and performance comparable to Deepseek V3.2, despite ...
An open-source project demonstrates a fully local home automation voice assistant, powered by Qwen3 models for ASR, LLM, and TTS. The system runs on an RTX 5060 Ti GPU with 16GB VRAM, highlighting the feasibility of on-prem AI implementations even wi...
A recent update to llama.cpp appears to improve support for the Qwen language model. This development could facilitate the execution and inference of large models on local hardware, opening new possibilities for on-premise applications and resource-c...
A user reported a positive experience with the Ministral-3-3B model, highlighting its effectiveness in running tool calls and its ability to operate with only 6GB of VRAM. The model, in its instruct version and quantized to Q8, proves suitable for re...
An enthusiast has developed a tool to visualize the internal architecture of large language models (LLMs) saved in .gguf format. The goal is to make the structure of these models more transparent, traditionally considered "black boxes". The tool allo...
A user reported on Reddit ongoing activity on GitHub related to improvements for llama.cpp, a framework for large language model inference. Specific details of the improvements are not provided, but the activity suggests active development of the pro...
Llama3pure offers developers lightweight, dependency-free machine learning inference engines for C, Node.js, and JavaScript. Ideal for those looking to better understand inference on local hardware, the project aims to provide a simple and direct alt...
Verity is an AI search and answer engine that runs fully locally on AI-powered PCs, leveraging CPU, GPU, and NPU acceleration. Optimized for Intel AI PCs using OpenVINO and Ollama, it offers self-hosted search via SearXNG and fact-based answers.
A local LLM user shares their experience using these models for development and search tasks, prompting the community to share further applications and use cases. The discussion focuses on the benefits of local execution and the various possible impl...
A LocalLLaMA user has developed an alternative benchmarking method for evaluating the real-world performance of large language models (LLMs) locally. Instead of focusing on tokens generated per second, the benchmark measures the total time required t...
AI expert Vishal Sikka warns about the limitations of LLMs operating in isolation. According to Sikka, these architectures are constrained by computational resources and tend to hallucinate when pushed to their limits. The proposed solution is to use...
A user compared DeepSeek-V2-Lite and GPT-OSS-20B on a 2018 laptop with integrated graphics, using OpenVINO. DeepSeek-V2-Lite showed almost double the speed and more consistent responses compared to GPT-OSS-20B, although with some logical and programm...
Releases of Kimi-Linear-48B-A3B and Step3.5-Flash compatible with llama.cpp are now available. Official GGUF files are not yet available, but the community is already working on their creation. The availability of these models expands options for loc...
Geodesic Attention Engine (GAE) is an open-source kernel that promises to drastically reduce memory consumption for large language models. With GAE, it's possible to handle 1 million tokens with only 1GB of VRAM, achieving significant energy savings ...
A user demonstrates how to run LLM models and Stable Diffusion on an old CPU-only desktop PC, paving the way for low-cost AI experimentation with full data control. The article explores the potential of AI inference on modest hardware, highlighting t...
The llama.cpp library has integrated support for Kimi-Linear, a technique that promises to improve the performance of language models. The integration was made possible by a pull request on GitHub, opening new possibilities for efficient inference.
A user demonstrates how to run a 16 billion parameter LLM on a 2018 HP ProBook laptop with an 8th generation Intel i3 processor and 16GB of RAM. By optimizing the use of the iGPU and leveraging MoE models, surprising inference speeds are achieved, op...
