Strix Halo Benchmarks New LLMs

🔑 Enhanced Key Takeaways

• •Strix Halo, or Ryzen AI Max+ 395, features 16 Zen 5 CPU cores, 32 threads, Radeon 8060S iGPU with 40 RDNA 3+ Compute Units, and up to 120 TOPS total AI performance, enabling strong local LLM inference with 128GB RAM support[1][3][4].
• •Benchmarks on Reddit demonstrate llama.cpp running quantized LLMs like Minimax M2.5, Step 3.5 Flash, Qwen3-Coder-Next, GLM 4.6V/4.7 Flash, and GPT-OSS-120B at 30k context on Ryzen AI Max+ 395 at 70W using ROCm 7.2[article].
• •The processor excels in multi-threaded tasks, outperforming NVIDIA DGX Spark's Arm CPU by 11% in Geekbench 6 multi-threaded scores, suitable for AI and general computing[1].
• •Configurable TDPs from 45W to 120W (e.g., Balanced ~85W, Max ~120W, Quiet ~55W) make it versatile for compact workstations like Corsair AI Workstation 300 and mini PCs[1][4].
• •Strix Halo debuted around early 2026, popular for mini PCs, high-end laptops, and gaming handhelds due to iGPU rivaling RTX 4060 Laptop GPU performance[1][2][3][4].

🛠️ Technical Deep Dive

• •16 full Zen 5 cores (no Zen 5c), up to 5.0 GHz boost, 16% IPC uplift over Zen 4 via branch prediction and refinements; supports AVX-512 for AI/scientific workloads[1][3][4].
• •Radeon 8060S iGPU: 40 Compute Units RDNA 3+, clocked at 2900 MHz (future Gorgon Halo refresh to 3+ GHz), rivals discrete RTX 4060 Laptop GPU in gaming/AI[2][3][5].
• •XDNA 2 NPU at 50 TOPS, total system AI up to 120 TOPS; LPDDR5x-8000 RAM support, PCIe 4, USB4; large L3 cache[3][4].
• •ROCm 7.2 enables llama.cpp LLM inference with 30k context on 128GB RAM pools; configurable power profiles via firmware (45-120W TDP)[1][article].
• •Tested in Ubuntu 24.04 for Geekbench; strong in parallel tasks like code compilation, content creation[1].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline