Can a MacBook Pro / Mac Studio M2 Max (32GB) run Llama 3.2 1B?

Yes — Llama 3.2 1B runs at FP16 (2.5 GB) at about 64–97 tok/s on a MacBook Pro / Mac Studio M2 Max (32GB).

How much memory does Llama 3.2 1B need?

Llama 3.2 1B needs about 1.7 GB at 4K context at its smallest quantization. A MacBook Pro / Mac Studio M2 Max (32GB) has 32.0 GB.

Yes — Llama 3.2 1B runs on a MacBook Pro / Mac Studio M2 Max (32GB) at FP16

Yes — it runs great.

Runs great

Best fit: Llama 3.2 1B at FP16 · 2.5 GB weights · 64–97 tok/s on a MacBook Pro / Mac Studio M2 Max (32GB).

Arena Text Overall · style-controlled

1,111FP16 untested

llama-3.2-1b-instruct · published configuration reference · FP16 not tested

Arena Text Overall (style-controlled), exact published entry llama-3.2-1b-instruct, 2026-10-08: reference rating 1,111.1. Published confidence bounds 1,103.2–1,119. 8,045 votes. Published llama-3.2-1b-instruct configuration reference. Reasoning mode is not disclosed by the Arena dataset. Tested precision, runtime and sampling settings are not disclosed in this dataset. Selected FP16 has not been measured by this Arena entry. No quantization quality adjustment is applied.

Snapshot published 2026-10-08 · snapshot imported 2026-10-11 UTC · validated last-good data · manual refresh

Arena leaderboard dataset · Arena / lmarena-ai, Arena Leaderboard Dataset (CC BY 4.0). Selected Text Overall style-controlled rows; field names adapted, ratings unchanged. · dataset CC BY 4.0 · revision e16f90232986

Total memory at 4K context: 3.4 GB; Weights + KV cache + runtime overhead
Estimated generation: 64–97 tok/s
Estimated first token: ~1.2s

Every quantization on this machine

On small screens, each quant lists every metric below its name. On wider screens, scroll sideways for total memory, speed and fit. Keyboard: focus the table, then use the arrow keys. Quant names stay visible.

Llama 3.2 1B quantizations on MacBook Pro / Mac Studio M2 Max (32GB), estimated at 4K context. Weights and total memory are shown separately.
Quant	Weights	Total memory	Speed	Fit
Q4_K_M	0.8 GB	1.7 GB	107–161 tok/s	Runs great
Q5_K_M	0.9 GB	1.8 GB	103–155 tok/s	Runs great
Q6_K	1.0 GB	2.0 GB	99–148 tok/s	Runs great
Q8_0	1.3 GB	2.3 GB	89–134 tok/s	Runs great
FP16	2.5 GB	3.4 GB	64–97 tok/s	Runs great

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