Local AI Hardware and Inference
This page tracks local inference, AMD ROCm, Strix Halo, Ryzen AI, llama.cpp, vLLM, Triton attention, Unsloth Studio, and hardware-specific benchmarks.
Sources in this batch
- Ubuntu and AMD provide practical ROCm/Ryzen AI/Gemma support material.
- TALOS-V2 is a hardware implementation of transformers running MicroGPT at very high token rates.
- Local coding-agent benchmarks on Strix Halo/R9700 and Strix Halo GPU benchmark pages provide empirical local-performance evidence.
- vLLM’s Triton attention backend deep dive, llama.cpp with ROCm, and Unsloth Studio document the software stack that makes local inference practical.
- TurboQuant connects extreme compression to deployment efficiency.
Research interest
The key research-relevant shift is that local AI is no longer just “smaller model on worse hardware”; it is becoming a systems research problem where architecture, quantization, memory bandwidth, kernels, and developer workflow co-evolve. Strix Halo and ROCm sources are interesting because they test whether consumer/workstation hardware can support agentic workloads, not just single prompt demos.
Open questions:
- Which workloads are bottlenecked by memory bandwidth, kernel support, context length, or model architecture?
- Can coding agents run productively on local hardware with acceptable latency?
- How should benchmarks capture end-to-end agent work rather than isolated tokens/sec?