Running DeepSeek V4.1 Flash Locally on AMD Strix Halo: Architecture, Setup, and Performance

This episode covers adding DeepSeek V4.1 Flash support to the Dwarf Star engine, which is now available as a ready-to-run container through AI Toolbox Cockpit. I walk through how to run this ~552B-parameter mixture-of-experts model locally on one or two Strix Halo devices, and explain what changed architecturally compared to previous DeepSeek releases.

■ Architecture & Design
- Why the model name understates the change: a causal encoder / decoder split that halves prefill compute
- Attention memory reductions, including shared global keys/values and 4-bit FP4 cache storage
- N-gram tables: longer n-grams, larger tables, and where the table information is injected

■ Setup & Performance
- Quantization format used, total file size, and why a single 128 GB device needs SSD streaming
- Single-host versus two-node cluster configuration in AI Toolbox Cockpit
- Long prefill and decode speed figures for both configurations, plus Terminal Bench Mini results

■ What's Next
- Upcoming Strix Halo refresh with 192 GB unified memory and expected speculative decoding support

If you run local models, want to explore agentic and coding workloads on a single machine, or are curious about efficient large-model inference, this gives you the concrete setup steps, measured numbers, and the reasoning behind the model's design choices so you can try it yourself.