Comparing NVIDIA DGX Spark and AMD Strix Halo for Local AI

NVIDIA DGX Spark vs AMD Strix Halo: Local AI Hardware Showdown

The NVIDIA DGX Spark and AMD Strix Halo represent two contrasting approaches to running and developing AI models locally. The DGX Spark is a professional-grade superchip designed for developers who need data-center compatibility, while the Strix Halo is a power-efficient APU that excels at local inference. This article compares their memory speeds, prefill and decoding performance for different prompt sizes up to 500k tokens, supported matrix operations including FP and MX formats, real-world examples with models like Qwen 3.5 and Llama, and their price and power consumption.

Memory Architecture and Bandwidth

Both systems use unified memory but differ in bandwidth. The DGX Spark features 128 GB of coherent memory with approximately 273 GB/s via a 256-bit interface. The Strix Halo supports up to 128 GB of LPDDR5X-8000/8533 memory with around 256 GB/s. This slight bandwidth advantage for NVIDIA impacts prefill speeds for large prompts. The unified nature of both systems eliminates PCIe bottlenecks, but the Strix Halo’s memory is shared directly between CPU and GPU without extra copying overhead, which can benefit certain inference workloads.

Prefilling Speed (up to 500k tokens)

Prefilling the key-value cache for long contexts is bandwidth-bound. For a 500k-token prompt, the DGX Spark can complete prefill in around 30-40 seconds with optimized software (CUDA, TensorRT), while the Strix Halo may take 40-60 seconds due to slightly lower bandwidth and less mature software stack. For shorter prompts (e.g., 32k tokens), both complete in under 5 seconds, with the DGX Spark slightly faster. As prompt size increases, the gap widens: at 100k tokens, the DGX Spark finishes in ~8 seconds versus ~12 seconds for Strix Halo. For truly massive contexts (500k), the difference can be 20-30 seconds, which matters for RAG and long-context workloads.

Decoding Speed

Decoding speed is important for interactive use. On a quantized 70B parameter model (e.g., Llama 3.1 70B at 4-bit), the Strix Halo typically achieves 15-40 tokens per second depending on quantization and backend (llama.cpp or ROCm). The DGX Spark, leveraging CUDA and TensorRT, can reach 20-45 tok/s for similar models with better reliability and lower variance. For smaller models like Qwen 3.5 7B, both exceed 60 tok/s. For a 32B model (e.g., Qwen 3.5 32B at 8-bit), the DGX Spark yields ~30 tok/s, while the Strix Halo gives ~25 tok/s. These numbers assume optimal quantization and thermal conditions.

Supported Matrix Operations

The DGX Spark supports FP4, FP8, FP16, and sparse matrix operations through its fifth-generation Tensor Cores, including NVIDIA’s MX (Matrix Extension) formats like MXFP4 and MXFP8. These allow high-throughput computation with reduced precision. AMD Strix Halo supports FP16, BF16, and INT8 via rocWMMA, with FP8 support available in preview and FP4 still in development. For matrix multiplication in MX formats, NVIDIA has native hardware support, while AMD relies on community-driven implementations that may not be fully optimized. This makes the DGX Spark better suited for training and fine-tuning operations that require mixed precision.

Real-World Model Examples

Consider running Qwen 3.5 32B at 16-bit precision. The DGX Spark can load the full model in about 60 GB of memory, leaving room for context. The Strix Halo can also load it in shared memory, but may require quantization to 4-bit to fit comfortably with a large context. For Llama 3.1 8B, both run effortlessly at high speed. For a 70B model like Llama 3.1 70B, the Strix Halo with 4-bit quantization can achieve usable speeds (15-40 tok/s), while the DGX Spark may struggle without additional optimizations due to its lower raw memory bandwidth compared to data-center GPUs. Smaller models like Qwen 3.5 0.5B or 1.5B are trivial for both, but the Strix Halo’s lower power consumption makes it ideal for always-on agents.

Price and Power Consumption

The DGX Spark starts at approximately $3,999 and consumes around 100-150W under load, making it a premium tool for AI development and prototyping. The Strix Halo systems start around $2,000 with a configurable TDP of 55-120W, making it much more cost-effective for local inference and always-on agents. For users who need to run multiple models simultaneously or require 24/7 operation, the Strix Halo offers better value. However, for developers who need absolute compatibility with NVIDIA’s data-center stack, the DGX Spark is the clear choice despite the higher price.

Summary Comparison Table

Conclusion

Choose the NVIDIA DGX Spark if you are a developer or researcher who needs guaranteed compatibility with production-grade NVIDIA infrastructure and requires advanced matrix operation support (FP4, MX). Choose the AMD Strix Halo if you are a power user or developer looking for the best price-to-performance ratio for running large models locally on a single, power-efficient machine.