Can I Run Mistral Small 3.2 on Apple M2?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 6 września 2026
Yes, but it's tight — Mistral Small 3.2 24B at Q4_K_M needs 16.7 GB at 8K context (14.2 GB weights + 2.5 GB KV cache/overhead), leaving ~1.3 GB of the Apple M2's 18 GB usable memory, at ~4 tok/s (est.), with room for up to 8K context.
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Apple M2 Specs
| VRAM | 24 GB unified memory |
| Memory Bandwidth | 100 GB/s |
Mistral Small 3.2 24B on the Apple M2: VRAM by quantization
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 49.6 GB | ✗ No | — | — |
| Q8_0 | 27.5 GB | ✗ No | — | — |
| Q6_K | 21.8 GB | ✗ No | — | — |
| Q5_K_M | 19.2 GB | ✗ No | — | — |
| Q4_K_M | 16.7 GB | ✓ Yes | 8K | — |
| Q3_K_M | 12.5 GB | ✓ Yes | 32K | — |
| Q2_K | 10.2 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache, on an estimated architecture — this model publishes no config we can read. 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 Q4_K_M on the Apple M2: 8K 16.7 GB · 32K 21.6 GB ✗ · 128K 41.4 GB ✗. Past 32K 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? Mistral Small 3.2 24B on 16 GB of system RAM, CPU only: runs, but it is tight.
Mistral Small 3.2 Sizes That Fit the Apple M2 (at 8K context)
| Mistral Small 3.2 24B | Q4_K_M · 16.7 GB at 8K context · ~4 tok/s (est.) |
Mistral Small 3.2 needs ~17 GB but this GPU has 24 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:
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FAQ
Mistral Small 3.2 on the Apple M2 — does it fit?
Yes, but it's tight — Mistral Small 3.2 24B at Q4_K_M needs 16.7 GB at 8K context (14.2 GB weights + 2.5 GB KV cache/overhead), leaving ~1.3 GB of the Apple M2's 18 GB usable memory, at ~4 tok/s (est.), with room for up to 8K context.
Which Mistral Small 3.2 variant fits best on the Apple M2?
Mistral Small 3.2 24B at Q4_K_M quantization needs 16.7 GB at 8K context (14.2 GB weights + 2.5 GB KV cache/overhead), estimated ~4 tokens/sec, up to 8K context.
Mistral Small 3.2 on Other GPUs
- Mistral Small 3.2 on NVIDIA GeForce RTX 4090
- Mistral Small 3.2 on NVIDIA GeForce RTX 3090
- Mistral Small 3.2 on AMD Radeon RX 7900 XTX
- Mistral Small 3.2 on Apple M4 Pro
- Mistral Small 3.2 on Apple M3
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? | Mistral Small 3.2 Model Page | Apple M2 GPU Page | VRAM calculator | Check Your Hardware