Can I Run Llama 3.3 on Apple M3 Pro?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026

Yes, comfortably — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), leaving ~3.3 GB of the Apple M3 Pro's 27 GB usable memory, at ~4 tok/s (est.).

See what else this hardware can run →

Kup ten sprzęt Apple MacBook Pro M3 Pro — 36 GB VRAM · 30 W board powerUruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

Apple MacBook Pro M3 Pro
36 GB VRAM · 30 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

Apple M3 Pro Specs

VRAM36 GB unified memory
Memory Bandwidth153 GB/s

Llama 3.3 70B Instruct on the Apple M3 Pro: VRAM by quantization

QuantVRAM neededFits 36 GB?Max contextWhole PC
F16143.5 GB✗ No——
Q8_077.9 GB✗ No——
Q6_K60.9 GB✗ No——
Q5_K_M53.1 GB✗ No——
Q4_K_M45.7 GB✗ No——
Q3_K_M33.3 GB✗ No——
Q2_K26.5 GB✓ Yes8K—

Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 36 GB of unified memory. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.

Superseded model. Llama 3.3 has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. Llama 4 needs at least 68.2 GB at Q4_K_M with an 8K context, so it does not fit the Apple M3 Pro.

Context costs VRAM too. At Q2_K_XS (Tight) on the Apple M3 Pro: 8K 23.7 GB · 32K 31.7 GB ✗ · 128K 64 GB ✗. Past 32K it no longer fits 36 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.

No compatible GPU? Llama 3.3 70B Instruct on 256 GB of system RAM, CPU only: runs, but too slowly to be worth it.

Llama 3.3 Sizes That Fit the Apple M3 Pro (at 8K context)

Llama 3.3 70B InstructQ2_K_XS (Tight) · 23.7 GB at 8K context · ~4 tok/s (est.)
Won't fit — rent Llama 3.3 instead

Llama 3.3 needs ~24 GB but this GPU has 36 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:

Affiliate links — we may earn a commission if you sign up, at no extra cost to you.

RunPod $0.34/hr
Rent on RunPod →
Vast.ai $0.35/hr · typical low · varies
Rent on Vast.ai →

Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.

FAQ

Can I run Llama 3.3 on the Apple M3 Pro?

Yes, comfortably — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), leaving ~3.3 GB of the Apple M3 Pro's 27 GB usable memory, at ~4 tok/s (est.).

What's the best Llama 3.3 size for the Apple M3 Pro?

Llama 3.3 70B Instruct at Q2_K_XS (Tight) quantization needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), estimated ~4 tokens/sec.

Llama 3.3 on Other GPUs

Popular Models on the Apple M3 Pro

VRAM Tier

Troubleshooting

Buying Guide

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