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.
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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.
