M5 MacBook Pro vs DGX Spark vs Strix Halo vs RTX 6000: Memory Bandwidth Drives Performance
Use the published repo to compare M5, DGX Spark, Strix Halo, and RTX 6000, noting M5’s superior memory bandwidth and performance over DGX Spark.
Run the benchmark repo to evaluate your hardware against M5, DGX Spark, Strix Halo, and RTX 6000, focusing on memory bandwidth and thermals.
Summary
A benchmark comparing the Apple M5 MacBook Pro, NVIDIA DGX Spark, AMD Strix Halo, and NVIDIA RTX 6000 was conducted over three days with standardized tests, and the results are published in a GitHub repository. The RTX 6000 offers ~1,800 gb/s memory bandwidth, while the M5 provides ~600 gb/s and the Spark and Strix Halo deliver ~256 gb/s, directly influencing tokens per second. The M5 MacBook Pro outperforms the DGX Spark by a significant margin due to its higher memory bandwidth, and its thermals remain stable at ~80 °C over extended runs. The M5’s performance advantage is attributed to its 2×+ memory bandwidth compared to the Spark, despite both using unified memory.
EVO X2 thermals were problematic during extended runs, whereas the MacBook’s thermals were surprisingly stable, although it produced a noticeable fan noise. The RTX 6000 is not equivalent to the RTX 5090, but shares many similarities that can inform hardware decisions for local AI workloads. The GitHub repo contains raw data and numbers for future discussions and debates, providing a useful reference for comparing GPU performance across different ecosystems. The study emphasizes that memory bandwidth and thermals are critical factors when choosing hardware for local AI inference.
Key changes
- RTX 6000 memory bandwidth ~1,800 gb/s
- M5 memory bandwidth ~600 gb/s
- Spark and Strix Halo ~256 gb/s
- M5 outperforms DGX Spark due to higher bandwidth
- M5 MacBook Pro thermals stable at ~80 °C
- EVO X2 thermals problematic
- RTX 6000 not same as RTX 5090
- Repo available at https://github.com/Light-Heart-Labs/MMBT-Messy-Model-Bench-Tests/tree/main/hardware-tests