Run Muse Spark Open on Apple Silicon
Muse Spark Open is Meta's 816 billion parameter mixture of experts model with 42B active per token and a 1M token context, released with open weights on 15 October 2026. The 4 bit version needs about 493 GB, more than any single Mac holds with room for context, so Apple ran it on two M5 Ultra Mac Studios with 512 GB each at 25 tokens per second. One Mac Studio with 256 GB holds the 2 bit version.
How much memory does Muse Spark Open need?
Weight size is parameter count × bits per weight; recommended memory adds 15% runtime headroom plus a KV-cache allowance for the 1,048,576-token context window.
| Quantization | Weights | Recommended RAM | Cheapest fit | Notes |
|---|---|---|---|---|
| Q4_K_M | ~494.7 GB | 584 GB | Thunderbolt 5 cluster | Best quality/size trade-off — the default for most deployments |
| Q8_0 | ~867 GB | 1008 GB | Thunderbolt 5 cluster | Near-lossless; noticeably better on code and long reasoning |
| FP16 | ~1632 GB | 1888 GB | Thunderbolt 5 cluster | Full precision — research and eval baselines |
Realistic configs: 2 × M5 Ultra Mac Studio with 512 GB each over Thunderbolt 5 for the 4 bit version (Apple's setup), or 1 × M3 Ultra with 256 GB for the 2 bit GGUF.
Which Mac should you pick?
Generation speed on Apple Silicon scales mostly with memory bandwidth, so the chip choice sets both whether the model fits and how fast it runs.
| Chip | Max RAM | Bandwidth | Fits Muse Spark Open? | From |
|---|---|---|---|---|
| M4 · Mac mini | 32 GB | 120 GB/s | Cluster only | $99/mo |
| M4 Pro · Mac mini | 64 GB | 273 GB/s | Cluster only | $200/mo |
| M4 Max · Mac Studio | 128 GB | 546 GB/s | Cluster only | $286/mo |
| M3 Ultra · Mac Studio | 256 GB | 819 GB/s | Cluster only | $571/mo |
Deploy Muse Spark Open in about 5 minutes
- 1
Build a Mac in the constructor — pick your cluster config with at least 584 GB of unified memory.
- 2
Pick any Ollama-based stack (or a clean machine) and pull Muse Spark Open with one command — every deployment ships the OpenAI-compatible API.
- 3
Point your existing OpenAI SDK code at the new base_url and ship.
from openai import OpenAI
client = OpenAI(
api_key="mcy_live_...",
base_url="https://dep-xxxx.macyou.cloud/v1",
)
response = client.chat.completions.create(
model="muse-spark-open",
messages=[{"role": "user", "content": "Hello!"}],
)License and what it's good at
Muse Model License — Muse Model License: Meta describes it as limited commercial terms that let many developers use the model for free. Read the license file before you build a product on it. Made by Meta.
- Meta calls it the most capable US open weight model to date
- Mixture of experts: 42B active parameters per token
- 1M token context and image input
Typical workloads: coding and agent backends that must stay private; evaluating a frontier class open model on your own hardware; long document and codebase analysis.
Frequently asked questions
Can one Mac run Muse Spark Open?
Only at 2 bits: the 2 bit GGUF fits a Mac Studio with 256 GB. The 4 bit version needs about 493 GB of weights, so Apple used two M5 Ultra Mac Studios with 512 GB each.
How fast is it on Macs?
Apple measured 25 tokens per second on two M5 Ultra Mac Studios with MLX. Each token reads only the 42B active parameters, which is why an 816B model runs at that speed.
Which runtimes support it?
Meta's model card lists SGLang, vLLM, Hugging Face, TensorRT LLM, Unsloth and llama.cpp, and says the model is optimized to run on two M5 Ultra Mac Studios with MLX.