标签 · Local LLM — Glean

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07-12

AMD Ryzen AI Halo Review: A 'Batteries Included' Dev Kit for AI

LTT Labs delivers a hands-on deep dive into the AMD Ryzen AI Halo, a $4,000 mini-PC packing the Ryzen AI Max+ 395 (16-core Zen 5, Radeon 8060S iGPU, XDNA 2 NPU) with 128 GB unified LPDDR5x-8000 memory (256 GB/s). The review benchmarks LLM inference (Qwen 3.6, Gemma 4, GLM 4.7) via llama-bench against M2/M3 Ultra Mac Studios and the Framework Desktop, focusing on prompt processing and token generation at increasing context sizes. The real differentiator: AMD's curated 'Best Known Configurations' and AI Playbooks that eliminate dependency hell for ROCm/PyTorch workflows. It also includes real-world power draw, thermal data, and a detailed USB-C PD negotiation analysis using an Infineon CY4500. A solid reference for engineers evaluating local AI development hardware.

www.lttlabs.com · 25 min · AMD · Developer Hardware · Llama Bench

06-30

Practical Guide to Setting Up a Local Coding Agent Stack with Open-Weight Models

This is a step-by-step tutorial for building a fully local coding agent using open-weight LLMs (primarily Qwen3.6 35B-A3B) served via Ollama and the Qwen-Code harness. The author covers model selection, speed/memory benchmarking with a custom script, a small agent capability evaluation (5 tasks), and a security audit checklist before running any harness. It then compares the same local model across three harnesses—Qwen-Code, Codex (open-source), and Claude Code—finding that Codex achieves the same task success rate with roughly half the token usage of Claude Code. The guide also explains SSH tunneling to run the model on a dedicated machine (e.g., DGX Spark) while using the harness on the main workstation. Targeted at engineers comfortable with the CLI who want a transparent, inspectable, and free alternative to proprietary coding agents.

magazine.sebastianraschka.com · 45 min · Coding Agent · Local LLM · Ollama