Models / Muse Spark Open 816B-A42B / VRAM
How much VRAM do you need for Muse Spark Open 816B-A42B?
Last updated:
Weight math from the model card, plus every measured RigBase result for Muse Spark Open 816B-A42B. Leave extra room for context (KV cache).
Weight size by quant
| Quant | Bits/weight | Weights only |
|---|---|---|
| Q8_0 | 8.50 | 867.1 GB |
| Q6_K | 6.56 | 669.5 GB |
| Q5_K_M | 5.70 | 581.8 GB |
| Q4_K_M | 4.89 | 499.2 GB |
| Q3_K_M | 4.00 | 407.6 GB |
| Q2_K | 3.16 | 322.2 GB |
Bits-per-weight for GGUF rows: llama.cpp quantize README. Parameter count: Hugging Face launch post: ~816B total, ~42B active. We advise staying at Q4 or above for chat quality — see Is Q4 good enough?.
Measured speeds for Muse Spark Open 816B-A42B
| Hardware | Quant | Engine | tok/s |
|---|---|---|---|
| 4x Apple M5 Ultra (unified, Mac Studio) | NVFP4, 8,192-token context | MLX, 4-node tensor parallel | 36.73 |
| 2x Apple M5 Ultra (unified, Mac Studio) | NVFP4, 8,192-token context | MLX, 2-node tensor parallel | 24.99 |
| Apple M3 Ultra (unified) · 512 GB | oQ3e (3-bit), ~344 GB | MLX (oMLX / mlx-lm) | 21 |
| Apple M3 Ultra (unified) · 512 GB | Q2_K (imatrix), ~304 GB | llama.cpp | 18 |
| 2x Apple M5 Ultra (unified, Mac Studio) | NVFP4, 8,192-token context | MLX, 2-node pipeline parallel | 15.74 |
⚡ = speculative decoding (MTP, DFlash…). Full list: best home rig for Muse Spark Open 816B-A42B.
Related
All Muse Spark Open 816B-A42B builds VRAM + KV calculator By VRAM size GGUF vs AWQ vs FP8 vs NVFP4 How much system RAM?
FAQ
How much VRAM do you need for Muse Spark Open 816B-A42B?
Weights alone are about 499.2 GB at Q4_K_M (816B × 4.89 bits ÷ 8). Add memory for the KV cache (context) and the runtime. The smallest-memory measured result in RigBase is 512 GB (Apple M3 Ultra (unified)) at 18 tok/s. Going below Q4 usually hurts quality for chat; see the Q4 guide.
What is the fastest measured speed for Muse Spark Open 816B-A42B?
4x Apple M5 Ultra (unified, Mac Studio): 36.73 tok/s plain decode (NVFP4, 8,192-token context, MLX, 4-node tensor parallel).
Does system RAM replace VRAM?
Only for overflow. When the whole model fits in GPU or unified memory, system RAM barely matters for decode speed. See the system-RAM guide.
Referral link: if you sign up with Google and add $10, Runpod gives you and RigBase bonus credit (terms). It never affects our data.