AMD Strix Halo — Backend Benchmarks (Grid View)
Backend benchmarks for AMD Strix Halo measuring tokens-per-second throughput across 17 LLM configurations ranging from 6.7B to 228.7B parameters. Test...
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Hardware Performance Analysis
Summary
Andrea Pellegrini details building a local LLM inference stack on the AMD Ryzen AI Max+ 395 with 128GB unified memory. The article covers performance benchmarks using Vulkan and ROCm backends for models up to 142B parameters.
Highlights
- Utilization of AMD Ryzen AI Max+ 395 with 128GB unified memory for local LLM inference.
- Performance benchmarks showing up to 884 tokens/s for Llama 2 7B Q4_0 using Vulkan.
- Achievement of 270 tokens/s for 120B parameter models at pp512 context length.
- Comparison of different inference backends including HIP, Vulkan, and ROCm.
- Demonstration of running large models up to 142B weights through hardware optimization.
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Context
Audience
AI Engineers and Hardware Enthusiasts
DomainMachine Learning Hardware
Formatlong-form article
Accessfree online
Topics
Related
AMD Strix HaloROCmVulkanHIPLLM QuantizationLocal LLM Inference
