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Can the Apple M3 Pro (18 GB) run Prometheus 2 7B?

68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 9.5 GB of 12.1 GB usable and makes one decision in about 15.1 s.

Best quant
Q8_0
Time per decision
15.1 s
Decisions per second
0.1/s
Input assumed
1,024 tokens
Returns
Written feedback, then a score

Best app to run it on the Apple M3 Pro (18 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX · use the MLX build

  • Runs the MLX build, typically 20–35% faster than GGUF for a model with 7.2B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open Prometheus 2 7B in LM Studio

  • MLX LM Runs the MLX build, typically 20–35% faster than GGUF for a model with 7.2B active parameters on Apple silicon.
  • Jan Runs the MLX build, typically 20–35% faster than GGUF for a model with 7.2B active parameters on Apple silicon.

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 Prometheus 2 7B

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/prometheus-eval/prometheus-7b-v2.0-GGUF (opens in a new tab)

ollama run hf.co/prometheus-eval/prometheus-7b-v2.0-GGUF

Original weights · Hugging Face

Full-precision files from Prometheus Eval, for fine-tuning, vLLM, Transformers.

huggingface.co/prometheus-eval/prometheus-7b-v2.0 (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 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, 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 Very slow
How well it runs on the Apple M3 Pro (18 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.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.30.6 / 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
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.2 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.9.0 / 15
Match score65.8 / 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 M3 Pro (18 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 6.2 GB 12.1 GB 8.5 s Very slow 64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 7.0 GB 12.1 GB 9.9 s Very slow 65Good quality (B) · 65 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 7.8 GB 12.1 GB 11.5 s Very slow 67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 9.5 GB 12.1 GB 15.1 s Very slow 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 16.3 GB 12.1 GB — Won't run 68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Times are for one request with a 1,024-token input and about 150 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does Prometheus 2 7B need on the M3 Pro 18GB?

At Q8_0 it needs about 9.5 GB. The M3 Pro 18GB has 12.1 GB usable.

How fast is Prometheus 2 7B on the M3 Pro 18GB?

It makes one decision in about 15.1 s at Q8_0, an estimate from the hardware and the size of the model.

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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