DGX Spark vs Strix Halo for Local LLMs: $4,699 vs $2,600 128GB Boxes

Short answer

Choose a Strix Halo box if your priority is:

• lowest cost per GB of unified memory
• Vulkan-based runtimes like llama.cpp and LM Studio
• a general-purpose Windows or Linux PC that also runs LLMs
• saving roughly $2,000 versus the Spark at the bottom of the range

Choose the DGX Spark if your priority is:

• CUDA and the NVIDIA software stack out of the box
• clustering two units over 200GbE for 200B–405B models
• FP4 model support and NVIDIA’s tuned containers
• matching the stack your cloud GPUs run

For single-user inference, speed does not decide this. Software and price do.

Specs that matter

NVIDIA lists the DGX Spark with 128GB LPDDR5x at 273 GB/s, about 1 petaFLOP of FP4 compute, and built-in ConnectX-7 200GbE networking. The box is spec’d for 240W power delivery. AMD’s Ryzen AI Max+ 395 uses quad-channel LPDDR5x-8000 for 256 GB/s, and up to 96GB is assignable to the GPU.

The price story, as of August 2026

Both boxes got more expensive. NVIDIA raised the Spark from $3,999 to $4,699 in February 2026 and cited memory supply, per Tom’s Hardware and NVIDIA’s developer forum. Strix Halo boxes floored around $2,000 pre-shortage; a July 31 sweep of 21 Amazon 128GB listings found the cheapest near $2,600, with mid-range units like the GMKtec EVO-X3 at $3,500 and up. The Register lists the HP Z2 Mini G1a at $2,949 as a nameable mid-range option.

So the real gap is about $2,000 only if you buy the cheapest Strix Halo listing. Against a $3,500 mid-range box, the Spark’s premium shrinks to about $1,200.

Performance: near parity, with one big asterisk

The Register benchmarked both platforms. The text-level findings: single-batch inference is close to parity, AMD is sometimes ahead on Vulkan runtimes, and both boxes struggle with dense 70B models. Community results put dense 70B decode around 2.6 tok/s on either box. That is a demo, not a daily driver.

Both boxes shine on MoE models. A Qwen3-30B-A3B-class model activates ~3B parameters per token, so 256–273 GB/s of bandwidth is enough for comfortable speeds while the 128GB pool holds the full weights. If you buy either box, plan your model list around MoE. Our 128GB RAM model guide covers the current picks.

The Spark’s unique trick is clustering. NVIDIA states two Sparks link over the built-in ConnectX-7 200GbE and handle models up to 405B parameters. Strix Halo has no equivalent turnkey path.

Software: CUDA convenience vs ROCm progress

The Spark runs NVIDIA’s stack natively: CUDA, TensorRT-LLM, tuned NIM containers. Things mostly work on day one.

ROCm on Strix Halo is workable but rougher. In the Register’s testing, most PyTorch scripts ran unmodified, but vLLM and FlashAttention-2 needed manual compilation. If you live in llama.cpp and LM Studio on Vulkan, you will barely notice. If you want vLLM serving or exotic runtimes, budget tinkering time.

Decision table

Final recommendation

For most OpenClaw users who want 128GB on a budget: Strix Halo, at the bottom of the price range, running MoE models on Vulkan.

Pay for the DGX Spark when the CUDA stack or the two-box clustering path has concrete value for you. And re-check both prices the week you buy. As of August 2026 the memory shortage moves them monthly.

Next steps

• Run the Local LLM Fit and Speed Estimator
• Best local LLMs for 128GB RAM
• Best local LLMs for 64GB RAM

Sources

• https://www.nvidia.com/en-us/products/workstations/dgx-spark/
• https://www.theregister.com/
• https://www.tomshardware.com/
• https://compute-market.com/