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Can the Apple M1 Ultra (64 GB) run Moonlight 16B-A3B Instruct?

46Fair quality (C) · 46 of 100: how capable the model is at F16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 36.6 GB of 48.0 GB usable, with an estimated speed of 51.3 tok/s.

Best quant
F16
Generation speed
51.3 tok/s
Prompt processing
4,340 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

Best app to run it on the Apple M1 Ultra (64 GB)

On macOS

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/moonshotai/Moonlight-16B-A3B-Instruct

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Moonlight 16B-A3B Instruct

Original weights · Hugging Face

Full-precision files from Moonshot AI, for fine-tuning, vLLM, Transformers.

huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 46Fair quality (C) · 46 of 100: how capable the model is at F16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the Apple M1 Ultra (64 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.20.7 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score75.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Apple M1 Ultra (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 11.1 GB 48.0 GB 189 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 12.5 GB 48.0 GB 167 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 14.4 GB 48.0 GB 145 tok/s Runs great 43Fair quality (C) · 43 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 16.0 GB 48.0 GB 128 tok/s Runs great 44Fair quality (C) · 44 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 17.8 GB 48.0 GB 115 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 21.6 GB 48.0 GB 92.8 tok/s Runs great 46Fair quality (C) · 46 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 36.6 GB 48.0 GB 51.3 tok/s Runs great 46Fair quality (C) · 46 of 100: how capable the model is at F16, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Moonlight 16B-A3B Instruct need on the M1 Ultra 64GB?

At F16 it needs about 36.6 GB. The M1 Ultra 64GB has 48.0 GB usable.

How fast is Moonlight 16B-A3B Instruct on the M1 Ultra 64GB?

About 51.3 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Moonlight 16B-A3B Instruct should I use on the M1 Ultra 64GB?

F16. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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