Yes — with 32.6 GB to spare

Llama 4 Scout 109B-A17B at Q4_K_M fits your M3 Max · 128 GB entirely in unified memory at 8K context, at an estimated 11 tokens per second. Past 703K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

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 32.6 GB of 96.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	107.9 GB	110.0 GB	—	~6.2	−0.1% ppl	14.0 GB over
Q6_K	83.2 GB	85.3 GB	235K	8.0	−0.4% ppl	Long context
Q5_K_M	71.9 GB	74.0 GB	476K	9.3	−0.8% ppl	Long context
Q4_K_M	61.3 GB	63.4 GB	703K	11	−1.9% ppl	Recommended
Q3_K_M	49.6 GB	51.7 GB	952K	13	−5.4% ppl	Long context
Q2_K	42.5 GB	44.6 GB	1024K	16	−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 96.0 GB of 128 GB. Raising it with iogpu.wired_limit_mb is possible, and risky.

03There is room to go to 703K context on this card.

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