Can I Run Llama 3.3 on Apple M1?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 lipca 2026
No — 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), and the Apple M1's 12 GB usable memory leaves it ~11.7 GB short. The cheapest card that runs it is the NVIDIA GeForce RTX 5090 Laptop GPU (24 GB).
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Apple M1 Specs
| VRAM | 16 GB unified memory |
| Memory Bandwidth | 68 GB/s |
Llama 3.3 70B Instruct on the Apple M1: VRAM by quantization
| Quant | VRAM needed | Fits 16 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 16 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 M1: 8K 23.7 GB ✗ · 32K 31.7 GB ✗ · 128K 64 GB ✗. Past 8K it no longer fits 16 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 M1 provides (16 GB).
Nearest GPU That Fits
NVIDIA GeForce RTX 5090 Laptop GPU (24 GB VRAM).
Llama 3.3 needs ~24 GB but this GPU has 16 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:
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FAQ
Will Llama 3.3 run on the Apple M1?
No — 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), and the Apple M1's 12 GB usable memory leaves it ~11.7 GB short. The cheapest card that runs it is the NVIDIA GeForce RTX 5090 Laptop GPU (24 GB).
What GPU should I get instead to run Llama 3.3?
The NVIDIA GeForce RTX 5090 Laptop GPU (24 GB VRAM) is the cheapest upgrade that fits it.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA GeForce RTX 5080
- Llama 3.3 on NVIDIA GeForce RTX 5070 Ti
- Llama 3.3 on NVIDIA GeForce RTX 5060 Ti 16GB
- Llama 3.3 on NVIDIA GeForce RTX 4080 Super
- Llama 3.3 on NVIDIA GeForce RTX 4080
Popular Models on the Apple M1
VRAM Tier
Troubleshooting
- Ollama: "model requires more system memory than is available"
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
← Can I Run It? | Llama 3.3 Model Page | Apple M1 GPU Page | VRAM calculator | Check Your Hardware