Can I Run Mistral Small 3.2 on NVIDIA GeForce RTX 5090?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes, comfortably — Mistral Small 3.2 24B at Q4_K_M needs 16.4 GB at 8K context (14.2 GB weights + 2.2 GB KV cache/overhead), leaving ~15.6 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~83 tok/s (est.), with room for up to 64K context.
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NVIDIA GeForce RTX 5090 Specs
| VRAM | 32 GB |
| Memory Bandwidth | 1792 GB/s |
Mistral Small 3.2 24B on the NVIDIA GeForce RTX 5090: VRAM by quantization
| Quant | VRAM needed | Fits 32 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 49.3 GB | ✗ No | — | — |
| Q8_0 | 27.2 GB | ✓ Yes | 32K | — |
| Q6_K | 21.5 GB | ✓ Yes | 64K | — |
| Q5_K_M | 18.9 GB | ✓ Yes | 64K | — |
| Q4_K_M | 16.4 GB | ✓ Yes | 64K | — |
| Q3_K_M | 12.2 GB | ✓ Yes | 64K | — |
| Q2_K | 9.9 GB | ✓ Yes | 128K | — |
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 32 GB. 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 NVIDIA GeForce RTX 5090: 8K 16.4 GB · 32K 20.4 GB · 128K 36.5 GB ✗. Past 128K it no longer fits 32 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 NVIDIA GeForce RTX 5090 (at 8K context)
| Mistral Small 3.2 24B | Q4_K_M · 16.4 GB at 8K context · ~83 tok/s (est.) |
At 2 hrs/day, buying (~$1,999) beats renting at $0.34/hr after about 8.2 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Does the NVIDIA GeForce RTX 5090 have enough VRAM for Mistral Small 3.2?
Yes, comfortably — Mistral Small 3.2 24B at Q4_K_M needs 16.4 GB at 8K context (14.2 GB weights + 2.2 GB KV cache/overhead), leaving ~15.6 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~83 tok/s (est.), with room for up to 64K context.
Which quantization of Mistral Small 3.2 should I use on the NVIDIA GeForce RTX 5090?
Mistral Small 3.2 24B at Q4_K_M quantization needs 16.4 GB at 8K context (14.2 GB weights + 2.2 GB KV cache/overhead), estimated ~83 tokens/sec, up to 64K context.
Mistral Small 3.2 on Other GPUs
- Mistral Small 3.2 on Apple M4
- Mistral Small 3.2 on Apple M2 Pro
- Mistral Small 3.2 on Apple M1 Pro
- Mistral Small 3.2 on Apple M5
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