What GPU do you need to run Meta Muse Spark Open 816B-A42B?
No single GPU. Meta Muse Spark Open 816B-A42B needs about 507 GB at UD-Q4_K_XL, more than any GPU, system or build we price.
822.04B parameters, 42B active (mixture-of-experts) (Hugging Face, accessed 2026-10-16). UD-Q4_K_XL GGUF: 505.5 GB (source). Architecture config (Hugging Face config.json; official repo is gated; config read from ungated mirror): 16 global attention layers × 16 KV heads × 64 head dim, plus 46 sliding-window layers (2048-token window), so about 64 KB of FP16 KV cache per token of context (how we estimate context).
Gated repo. Active params from the model name; release date from the Hugging Face model card and Meta's announcement. No plain Q4_K_M GGUF found, so the size is unsloth UD-Q4_K_XL.
Other places to get it
Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.
- ModelScope: Other uploaderSame account name as on Hugging Face; we found no link from the lab to it
- Ollama: Cloud only (runs on Ollama's servers)
ollama run muse-spark-open:cloud
No single GPU in our database fits it. Use multiple GPUs (see calculator).
Speed calculator
estimateEstimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works