Llama 4 Scout 17B-16E on Apple M2 Pro (32 GB)

Can the Apple M2 Pro (32 GB) run Llama 4 Scout 17B-16E?

6 pts

No, it doesn’t fit

No. Llama 4 Scout 17B-16E needs about 47.4 GB even at Q2_K; the Apple M2 Pro (32 GB) has 21.4 GB usable.

Download Llama 4 Scout 17B-16E

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run llama4:scout

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

huggingface.co/ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/llama-models (opens in a new tab)

git clone https://github.com/meta-llama/llama-models

Meta’s official Llama repo: model cards, prompt formats and download tools.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the F grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	6.8 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Total	6.8 / 100	6 ptsWon't run (F) · 6 of 100 points: Model quality 6.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 Pro (32 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	47.4 GB	21.4 GB	—	Won't run	6 ptsWon't run (F) · 6 of 100 points: Model quality 6.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF files (opens in a new tab)
`Q3_K_M`	57.1 GB	21.4 GB	—	Won't run	8 ptsWon't run (F) · 8 of 100 points: Model quality 8.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF files (opens in a new tab)
`Q4_K_M`	70.2 GB	21.4 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · part 1 of 2 (opens in a new tab)
`Q5_K_M`	81.0 GB	21.4 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF files (opens in a new tab)
`Q6_K`	93.0 GB	21.4 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF files (opens in a new tab)
`Q8_0`	119 GB	21.4 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15
`F16`	221 GB	21.4 GB	—	Won't run	9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	Original weights (opens in a new tab)

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

Hardware that can run it

85 ptsExcellent (S) · 85 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 64.7 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 9.8/15, Memory use 15.0/15 Apple M4 Max (128 GB) Q4_K_M · 17.0 tok/s
80 ptsGreat (A) · 80 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 11.8/15, Memory use 12.8/15 Apple M2 Ultra (192 GB) Q4_K_M · 24.9 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 7.4/15, Memory use 15.0/15 NVIDIA GB10 (128 GB) Q4_K_M · 9.9 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 10.1/15 Apple M3 Ultra (256 GB) Q4_K_M · 25.5 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 30.8/45, Memory fit 25.0/25, Speed 6.7/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) Q4_K_M · 8.0 tok/s

Models that do run on the Apple M2 Pro (32 GB)

91 ptsExcellent (S) · 91 of 100 points: Model quality 39.5/45, Memory fit 24.0/25, Speed 12.5/15, Memory use 15.0/15 Gemma 4 26B-A4B Q4_K_M · 27.8 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 35.3/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Qwen3.6 35B-A3B Q3_K_M · 45.6 tok/s
89 ptsExcellent (S) · 89 of 100 points: Model quality 35.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Qwen3.5 35B-A3B Q3_K_M · 45.6 tok/s
88 ptsExcellent (S) · 88 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GLM-4.7 Flash (30B-A3B) Q3_K_M · 45.6 tok/s
88 ptsExcellent (S) · 88 of 100 points: Model quality 34.0/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Ornith 1.5 35B-A3B Q3_K_M · 45.6 tok/s

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.