Yes — with 80.6 GB to spare

Llama 4 Scout 109B-A17B at Q4_K_M fits your M2 Ultra · 192 GB entirely in unified memory at 8K context, at an estimated 22 tokens per second. There is room for its full 1024K window.

Fully in unified memory 8K context Q4_K_M · 61.3 GB Llama 4 Community Released 5 Apr 2025 Vision

A 10M-token window on paper, chunked attention in practice (8K chunks on 3 of 4 layers). Needs 64 GB+ at Q4.

What hardware do I need for Llama 4 Scout 109B-A17B? →

The VRAM budget

weights 61.3 GB

Weights 61.3 GB KV cache @ 8K 1.50 GB Runtime overhead 0.6 GB Free 80.6 GB of 144.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	107.9 GB	110.0 GB	734K	12	−0.1% ppl	Long context
Q6_K	83.2 GB	85.3 GB	1024K	16	−0.4% ppl	Long context
Q5_K_M	71.9 GB	74.0 GB	1024K	19	−0.8% ppl	Long context
Q4_K_M	61.3 GB	63.4 GB	1024K	22	−1.9% ppl	Recommended
Q3_K_M	49.6 GB	51.7 GB	1024K	27	−5.4% ppl	Long context
Q2_K	42.5 GB	44.6 GB	1024K	31	−15% ppl	Long context

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (8192 tokens, 12 of 48 layers global), which is why its cache barely grows with context.

How to run it

Ollama llama.cpp LM Studio MLX

terminal

$ pip install mlx-lm
$ mlx_lm.generate --model mlx-community/Llama-4-Scout-17B-16E-Instruct-4bit \
    --max-tokens 512 --prompt "Hello"

Apple's own array framework. The fastest path on Apple Silicon. More on MLX.

01Download is 61.3 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02macOS caps what the GPU may wire down at about 144.0 GB of 192 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03There is room to go to the model's full 1024K context on this card.

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