Can I Run Llama 3.3 on Apple M2?
Written by Jakub Rusinowski · Last updated July 12, 2026
Only with offload — 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) against the Apple M2's 18 GB usable memory, so part of it spills onto CPU/system RAM and speed drops sharply.
Get a personalized upgrade path →
or compare on Vast.ai from $0.35/hr (typical low · varies)
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
Apple M2 Specs
| VRAM | 24 GB unified memory |
| Memory Bandwidth | 100 GB/s |
Llama 3.3 70B Instruct on the Apple M2: VRAM by quantization
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 143.5 GB | ✗ No | — | — |
| Q8_0 | 77.9 GB | ✗ No | — | — |
| Q6_K | 60.9 GB | ✗ No | — | — |
| Q5_K_M | 53.1 GB | ✗ No | — | — |
| Q4_K_M | 45.7 GB | ✗ No | — | — |
| Q3_K_M | 33.3 GB | ✗ No | — | — |
| Q2_K | 26.5 GB | ✗ No | — | — |
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 24 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 Q2_K_XS (Tight) on the Apple M2: 8K 23.7 GB ✗ · 32K 31.7 GB ✗ · 128K 64 GB ✗. Past 8K it no longer fits 24 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.
Why It Does Not Fit
Every Llama 3.3 variant requires more VRAM than the Apple M2 provides (24 GB).
Nearest GPU That Fits
Intel Arc Pro B70 (32 GB VRAM).
Llama 3.3 needs ~24 GB but this GPU has 24 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.
Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Does the Apple M2 have enough VRAM for Llama 3.3?
Only with offload — 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) against the Apple M2's 18 GB usable memory, so part of it spills onto CPU/system RAM and speed drops sharply.
Which GPU can actually run Llama 3.3?
The Intel Arc Pro B70 (32 GB VRAM) is the cheapest upgrade that fits it.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA GeForce RTX 4090
- Llama 3.3 on NVIDIA GeForce RTX 3090
- Llama 3.3 on AMD Radeon RX 7900 XTX
- Llama 3.3 on Apple M4 Pro
- Llama 3.3 on Apple M3
VRAM Tier
Troubleshooting
- Ollama: "model requires more system memory than is available"
- Out of memory at long context (the KV cache, not the weights)
Buying Guide
← Can I Run It? | Llama 3.3 Model Page | Apple M2 GPU Page | VRAM calculator | Check Your Hardware