Hey everyone!
I'm planning to build a small, portable computer dedicated to running local LLMs, AI agents, and coding workflows 24/7. I'm trying to decide whether a prebuilt mini PC or a custom SFF build would be the better option.
My main goal is to find the best balance between inference speed and model intelligence. I want a fast and responsive system, but I also want to run capable models that can reason properly, follow instructions, write good code, use tools reliably, and complete complex, multi-step tasks without constantly making mistakes.
I'm not interested in running a tiny model just to achieve high inference speeds if it struggles with relatively simple tasks.
What I want to do:
- Run Hermes Agent or OpenClaw for personal automation.
- Create multiple specialized AI agents for research, email management, and everyday tasks.
- Build AI study assistants for analyzing PDFs, explaining concepts, and generating quizzes.
- Use local LLMs for software development, coding, debugging, testing, and working on larger projects.
- Run coding agents locally without relying on cloud-based coding subscriptions.
- Run capable local models through Ollama, llama.cpp, or another optimized inference backend.
- Access and manage everything remotely from my laptop or phone.
- Expand the system with more advanced agents, integrations, and automation capabilities over time.
I want this mini PC to handle both AI automation and software development. Ideally, I'd like to run autonomous coding agents capable of working on projects, making changes, running tests, and debugging code locally.
Hardware preferences:
- Budget: Around $2,000–$3,500.
- Size: Ideally 1–5 liters, but I'm open to slightly larger SFF builds.
- RAM: 64 GB minimum, potentially 96–128 GB.
- Storage: 2 TB NVMe SSD.
- Quiet, power-efficient, and capable of running 24/7.
- Upgradeability would be a big plus.
I've been considering Ryzen AI Max+ 395 mini PCs, but I'm also interested in building something myself so I can upgrade components later.
Model preferences:
I'm open to Qwen, DeepSeek, GPT-OSS, GLM, Nemotron, Liquid AI, or any other capable local models.
Ideally, I'd like to run relatively powerful models in the 20B–35B range, preferably efficient MoE models if they offer better performance.
I'm looking for fast inference without sacrificing model quality, reasoning, coding capabilities, tool-calling reliability, or successful task completion.
My questions:
- Would you choose a Ryzen AI Max+ 395 mini PC or a custom SFF build with a dedicated GPU?
- Which local models offer the best balance of intelligence, coding ability, tool calling, and inference speed?
- Is 64 GB RAM sufficient for running coding agents and multiple AI agents, or would 96–128 GB be worth the additional cost?
- How much does context length affect performance when working on larger codebases and multi-step autonomous tasks?
- What inference backend and quantization would you recommend?
- Can local models in this hardware class reliably handle autonomous coding workflows, or would I need more powerful hardware?
- What kind of real-world performance should I realistically expect within my budget and size constraints?
I'd really appreciate hardware recommendations, real-world benchmarks, and experiences from people running local coding agents and automation systems.
Thanks!
