Can I Run Llama 3.2 Family on Apple M2 Max?
作者： Jakub Rusinowski · 最后更新： 2026年9月29日
Yes, comfortably — Llama 3.2 90B Vision Instruct at Q4_K_M needs 57.8 GB at 8K context (53.6 GB weights + 4.2 GB KV cache/overhead), leaving ~14.2 GB of the Apple M2 Max's 72 GB usable memory, at ~4 tok/s (est.), with room for up to 32K context.
See what else this hardware can run →
或在 Vast.ai 比较，低至 $0.77/小时 (typical low · varies)
作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。
Apple M2 Max Specs
| VRAM | 96 GB unified memory |
| Memory Bandwidth | 400 GB/s |
Llama 3.2 90B Vision Instruct on the Apple M2 Max: VRAM by quantization
| Quant | VRAM needed | Fits 96 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 181.8 GB | ✗ No | — | — |
| Q8_0 | 98.5 GB | ✗ No | — | — |
| Q6_K | 77 GB | ✗ No | — | — |
| Q5_K_M | 67.1 GB | ✓ Yes | 16K | — |
| Q4_K_M | 57.8 GB | ✓ Yes | 32K | — |
| Q3_K_M | 42 GB | ✓ Yes | 64K | — |
| Q2_K | 33.3 GB | ✓ Yes | 64K | — |
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 96 GB of unified memory. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the Apple M2 Max: 8K 57.8 GB · 32K 67.8 GB · 125K 106.8 GB ✗. Past 125K it no longer fits 96 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? Llama 3.2 Family on 8 GB of system RAM, CPU only: runs.
Llama 3.2 Family Sizes That Fit the Apple M2 Max (at 8K context)
| Llama 3.2 90B Vision Instruct | Q4_K_M · 57.8 GB at 8K context · ~4 tok/s (est.) |
| Llama 3.2 11B Vision Instruct | Q4_K_M · 8.5 GB at 8K context · ~28 tok/s (est.) |
| Llama 3.2 3B Instruct | Q4_K_M · 3.7 GB at 8K context · ~72 tok/s (est.) |
| Llama 3.2 1B Instruct | Q4_K_M · 1.8 GB at 8K context · ~142 tok/s (est.) |
Llama 3.2 Family needs ~58 GB but this GPU has 96 GB. Rent a A100 (80 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.
Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Is the Apple M2 Max enough to run Llama 3.2 Family locally?
Yes, comfortably — Llama 3.2 90B Vision Instruct at Q4_K_M needs 57.8 GB at 8K context (53.6 GB weights + 4.2 GB KV cache/overhead), leaving ~14.2 GB of the Apple M2 Max's 72 GB usable memory, at ~4 tok/s (est.), with room for up to 32K context.
What's the best Llama 3.2 Family size for the Apple M2 Max?
Llama 3.2 90B Vision Instruct at Q4_K_M quantization needs 57.8 GB at 8K context (53.6 GB weights + 4.2 GB KV cache/overhead), estimated ~4 tokens/sec, up to 32K context.
Every Llama 3.2 Family size on the Apple M2 Max
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| Llama 3.2 90B Vision Instruct | 57.8 GB | Runs | ~4 tok/s |
| Llama 3.2 11B Vision Instruct | 8.5 GB | Runs | ~28 tok/s |
| Llama 3.2 3B Instruct | 3.7 GB | Runs | ~72 tok/s |
| Llama 3.2 1B Instruct | 1.8 GB | Runs | ~142 tok/s |
Llama 3.2 Family on Other GPUs
- Llama 3.2 Family on NVIDIA RTX PRO 6000 Blackwell
- Llama 3.2 Family on NVIDIA A100 80GB (PCIe)
- Llama 3.2 Family on NVIDIA H100 80GB (PCIe)
- Llama 3.2 Family on Apple M4 Max
Popular Models on the Apple M2 Max
VRAM Tier
Troubleshooting
Buying Guide
← Can I Run It? | Llama 3.2 Family Model Page | Apple M2 Max GPU Page | VRAM calculator | Check Your Hardware