Best home rig for Muse Spark Open 816B-A42B
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Real, measured generation speeds for Muse Spark Open 816B-A42B on home hardware, fastest first. Different quants/engines aren't directly comparable.
| Build | Quant | Engine | tok/s | Cost | |
|---|---|---|---|---|---|
| 4x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open at 36.7 tok/s | NVFP4, 8,192-token context | MLX, 4-node tensor parallel | 36.73 | source | |
| 2x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open 816B | NVFP4, 8,192-token context | MLX, 2-node tensor parallel | 24.99 | source | |
| Mac Studio M3 Ultra 512GB: Muse Spark Open 816B (Hugging Face test) | oQ3e (3-bit), ~344 GB | MLX (oMLX / mlx-lm) | 21 | source | |
| Mac Studio M3 Ultra 512GB: Muse Spark Open 816B (Hugging Face test) | Q2_K (imatrix), ~304 GB | llama.cpp | 18 | source | |
| 2x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open 816B | NVFP4, 8,192-token context | MLX, 2-node pipeline parallel | 15.74 | source |
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FAQ
How much VRAM does Muse Spark Open 816B-A42B need?
The weights are about 499.2 GB at Q4_K_M (816B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is Apple M3 Ultra (512 GB) at 21 tok/s.
What is the fastest measured hardware for Muse Spark Open 816B-A42B?
4x Apple M5 Ultra: 36.73 tok/s generation (MLX, 4-node tensor parallel, NVFP4, 8,192-token context), from https://machinelearning.apple.com/research/mlx-distributed-inference.
Which GPUs have measured results for Muse Spark Open 816B-A42B?
2x Apple M5 Ultra, 4x Apple M5 Ultra, Apple M3 Ultra.
Can it run on… 2x Apple M5 Ultra · 4x Apple M5 Ultra · Apple M3 Ultra
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