The article discusses the growing trend of running Large Language Models (LLMs) locally on personal machines, exploring the motivations behind this shift – including privacy concerns, cost savings, and a desire for technological sovereignty – as well as the hardware and software advancements making it increasingly feasible.
An open source web crawler that searches the internet. It's a minimal, real-time web search CLI that searches the internet for you. Enter a query and get search results as JSON (title, url, published_date), sorted by recency.
Build, enrich, and transform datasets using AI models with no code. This repository provides the source code for Hugging Face AI Sheets, an open-source tool for dataset manipulation using AI.
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
SWE-agent is an open-source tool that utilizes large language models (LLMs) like GPT-4o and Claude Sonnet 3.5 to autonomously fix bugs in GitHub repositories, solve cybersecurity challenges, and perform complex tasks. It features a mode called EnIGMA for offensive cybersecurity and prioritizes simplicity and adaptability.
A Reddit thread discussing preferred local Large Language Model (LLM) setups for tasks like summarizing text, coding, and general use. Users share their model choices (Gemma, Qwen, Phi, etc.) and frameworks (llama.cpp, Ollama, EXUI) along with potential issues and configurations.
| **Model** | **Use Cases** | **Size (Parameters)** | **Approx. VRAM (Q4 Quantization)** | **Approx. RAM (Q4)** | **Notes/Requirements** |
|----------------|---------------------------------------------------|------------------------|-----------------------------------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Gemma 3 (Meta)** | Summarization, conversational tasks, image recognition, translation, simple writing | 3B, 4B, 7B, 8B, 12B, 27B+ | 2-4GB (3B), 4-6GB (7B), 8-12GB (12B) | 4-8GB (3B), 8-12GB (7B), 16-24GB (12B) | Excellent performance for its size. Recent versions have had memory leak issues (see Reddit post – use Ollama 0.6.6 or later, but even that may not be fully fixed). QAT versions are highly recommended. |
| **Qwen 2.5 (Alibaba)** | Summarization, coding, reasoning, decision-making, technical material processing | 3.5B, 7B, 72B | 2-3GB (3.5B), 4-6GB (7B), 26-30GB (72B) | 4-6GB (3.5B), 8-12GB (7B), 50-60GB (72B) | Qwen models are known for strong performance. Coder versions specifically tuned for code generation. |
| **Qwen3 (Alibaba - upcoming)**| General purpose, likely similar to Qwen 2.5 with improvements | 70B | Estimated 25-30GB (Q4) | 50-60GB | Expected to be a strong competitor. |
| **Llama 3 (Meta)**| General purpose, conversation, writing, coding, reasoning | 8B, 13B, 70B+ | 4-6GB (8B), 7-9GB (13B), 25-30GB (70B) | 8-12GB (8B), 14-18GB (13B), 50-60GB (70B) | Current state-of-the-art open-source model. Excellent balance of performance and size. |
| **YiXin (01.AI)** | Reasoning, brainstorming | 72B | ~26-30GB (Q4) | ~50-60GB | A powerful model focused on reasoning and understanding. Similar VRAM requirements to Qwen 72B. |
| **Phi-4 (Microsoft)** | General purpose, writing, coding | 14B | ~7-9GB (Q4) | 14-18GB | Smaller model, good for resource-constrained environments, but may not match larger models in complexity. |
| **Ling-Lite** | RAG (Retrieval-Augmented Generation), fast processing, text extraction | Variable | Varies with size | Varies with size | MoE (Mixture of Experts) model known for speed. Good for RAG applications where quick responses are important. |
**Key Considerations:**
* **Quantization:** The VRAM and RAM estimates above are based on 4-bit quantization (Q4). Lower quantization (e.g., Q2) will reduce memory usage further, but *may* impact quality. Higher quantization (e.g., Q8, FP16) will increase quality but require significantly more memory.
* **Frameworks:** Popular frameworks for running these models locally include:
* **llama.cpp:** Highly optimized for CPU and GPU, especially on Apple Silicon.
* **Ollama:** Simplified setup and management of LLMs. (Be aware of the Gemma 3 memory leak issue!)
* **Text Generation WebUI (oobabooga):** Web-based interface with many features and customization options.
* **Hardware:** A dedicated GPU with sufficient VRAM is highly recommended for decent performance. CPU-only inference is possible but can be slow. More RAM is generally better, even if the model fits in VRAM.
* **Context Length:** The "40k" context mentioned in the Reddit post refers to the maximum number of tokens (words or sub-words) the model can process at once. Longer context lengths require more memory.
| **Model** | **Use Cases** | **Size (Parameters)** | **Approx. VRAM (Q4 Quantization)** | **Approx. RAM (Q4)** | **Notes/Requirements** |
|----------------|---------------------------------------------------|------------------------|-----------------------------------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Gemma 3 (Meta)** | Summarization, conversational tasks, image recognition, translation, simple writing | 3B, 4B, 7B, 8B, 12B, 27B+ | 2-4GB (3B), 4-6GB (7B), 8-12GB (12B) | 4-8GB (3B), 8-12GB (7B), 16-24GB (12B) | Excellent performance for its size. Recent versions have had memory leak issues (see Reddit post – use Ollama 0.6.6 or later, but even that may not be fully fixed). QAT versions are highly recommended. |
| **Qwen 2.5 (Alibaba)** | Summarization, coding, reasoning, decision-making, technical material processing | 3.5B, 7B, 72B | 2-3GB (3.5B), 4-6GB (7B), 26-30GB (72B) | 4-6GB (3.5B), 8-12GB (7B), 50-60GB (72B) | Qwen models are known for strong performance. Coder versions specifically tuned for code generation. |
| **Qwen3 (Alibaba - upcoming)**| General purpose, likely similar to Qwen 2.5 with improvements | 70B | Estimated 25-30GB (Q4) | 50-60GB | Expected to be a strong competitor. |
| **Llama 3 (Meta)**| General purpose, conversation, writing, coding, reasoning | 8B, 13B, 70B+ | 4-6GB (8B), 7-9GB (13B), 25-30GB (70B) | 8-12GB (8B), 14-18GB (13B), 50-60GB (70B) | Current state-of-the-art open-source model. Excellent balance of performance and size. |
| **YiXin (01.AI)** | Reasoning, brainstorming | 72B | ~26-30GB (Q4) | ~50-60GB | A powerful model focused on reasoning and understanding. Similar VRAM requirements to Qwen 72B. |
| **Phi-4 (Microsoft)** | General purpose, writing, coding | 14B | ~7-9GB (Q4) | 14-18GB | Smaller model, good for resource-constrained environments, but may not match larger models in complexity. |
| **Ling-Lite** | RAG (Retrieval-Augmented Generation), fast processing, text extraction | Variable | Varies with size | Varies with size | MoE (Mixture of Experts) model known for speed. Good for RAG applications where quick responses are important. |
**Key Considerations:**
* **Quantization:** The VRAM and RAM estimates above are based on 4-bit quantization (Q4). Lower quantization (e.g., Q2) will reduce memory usage further, but *may* impact quality. Higher quantization (e.g., Q8, FP16) will increase quality but require significantly more memory.
* **Frameworks:** Popular frameworks for running these models locally include:
* **llama.cpp:** Highly optimized for CPU and GPU, especially on Apple Silicon.
* **Ollama:** Simplified setup and management of LLMs. (Be aware of the Gemma 3 memory leak issue!)
* **Text Generation WebUI (oobabooga):** Web-based interface with many features and customization options.
* **Hardware:** A dedicated GPU with sufficient VRAM is highly recommended for decent performance. CPU-only inference is possible but can be slow. More RAM is generally better, even if the model fits in VRAM.
* **Context Length:** The "40k" context mentioned in the Reddit post refers to the maximum number of tokens (words or sub-words) the model can process at once. Longer context lengths require more memory.
Details the development and release of DeepCoder-14B-Preview, a 14B parameter code reasoning model achieving performance comparable to o3-mini through reinforcement learning, along with the dataset, code, and system optimizations used in its creation.
ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
Goose is a local, extensible, open-source AI agent designed to automate complex engineering tasks. It can build projects from scratch, write and execute code, debug failures, orchestrate workflows, and interact with external APIs. Goose is flexible, supporting any LLM and seamlessly integrating with MCP-enabled APIs, making it a powerful tool for developers to accelerate innovation.
AGNTCY is building the Internet of Agents to be accessible for all, focusing on innovation, development, and maintenance of software components and services for agentic workflows and multi-agent applications.
**Discover:**
**1. Agent directory**
- Registry for agent publishing and discovery
- Tracks reputation and quality
**2. Open agent schema framework**
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
**Compose:**
**1. Agent connect protocol and SDK**
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
**What could these look like in action?**
A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
**Discover:**
**1. Agent directory**
- Registry for agent publishing and discovery
- Tracks reputation and quality
**2. Open agent schema framework**
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
**Compose:**
**1. Agent connect protocol and SDK**
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
**What could these look like in action?**
A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
