Choosing Between AMD Strix Halo and Nvidia DGX Spark for Home LLM Server
Benchmark both AMD Strix Halo and Nvidia DGX Spark with your target models to determine real‑world inference speed before purchasing.
Benchmark both AMD Strix Halo and Nvidia DGX Spark with your target models to determine real‑world inference speed before purchasing.
Summary
A user compares the AMD Strix Halo (128 GB AMD Ryzen AI Max+ 395 Framework Desktop) and Nvidia DGX Spark (Asus Ascent GX10) for a home LLM server. The target models include Gemma 4 31B, Gemma 4 26B A4B, Qwen 3.6 27B, Qwen 3.6 35B A3B, and GPT OSS 120B. The user plans to use Q4_K_M or Q6_K quantization to balance speed and quality, and to run models with long context lengths of 128 K and above. For the interface, Open WebUI or alternatives are considered, and LM Studio or llama.cpp are options for the engine. Ubuntu is chosen as the operating system.
The post highlights the lack of direct performance comparisons between the two hardware options and asks for real‑world inference speed data, especially with long contexts.
This information is valuable for developers deciding on hardware for local LLM inference and for those evaluating quantization strategies.
Key changes
- target models: Gemma 4 31B, Gemma 4 26B A4B, Qwen 3.6 27B, Qwen 3.6 35B A3B, GPT OSS 120B
- quantization options Q4_K_M and Q6_K
- Open WebUI or alternatives for interface
- LM Studio or llama.cpp for engine
- Ubuntu OS
- need to benchmark both hardware options