Best home rig for Muse Spark Open 816B-A42B

Will it fit my GPU? Best value GPU per euro →

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
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

Rent before you buy: try this model on a cloud GPU for a few dollars first: Runpod (referral link; sign up with Google to get the new-user bonus)

Memory math: how much VRAM / RAM does Muse Spark Open 816B-A42B need?

Muse Spark Open 816B-A42B has 816B parameters, about 42B active per token (source: Hugging Face launch post: ~816B total, ~42B active). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.

Format	Bits/weight	Weights	Smallest common memory size that holds the weights alone
Q8_0	8.50	867.1 GB	1024 GB
Q6_K	6.56	669.5 GB	1024 GB
Q5_K_M	5.70	581.8 GB	1024 GB
Q4_K_M	4.89	499.2 GB	512 GB
Q3_K_M	4.00	407.6 GB	512 GB
Q2_K	3.16	322.2 GB	512 GB

Bits-per-weight for GGUF quants are llama.cpp's own figures (llama.cpp quantize README). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the VRAM calculator.

Why it can still run on smaller GPUs: it is a mixture-of-experts model, so only about 42B of its 816B parameters are used for each generated token. Runtimes such as llama.cpp (--n-cpu-moe), ExLlamaV3 or Project Maya can keep most expert weights in system RAM and only the shared layers and hot experts in VRAM. Then you need VRAM + system RAM ≥ weights, and speed is limited mainly by RAM bandwidth.

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