Can I Run Codestral on NVIDIA GeForce RTX 5090?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes, comfortably — Codestral 22B at Q4_K_M needs 16.1 GB at 8K context (13.4 GB weights + 2.7 GB KV cache/overhead), leaving ~15.9 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~86 tok/s (est.), with room for up to 32K context.
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NVIDIA GeForce RTX 5090 Specs
| VRAM | 32 GB |
| Memory Bandwidth | 1792 GB/s |
Codestral 22B on the NVIDIA GeForce RTX 5090: VRAM by quantization
| Quant | VRAM needed | Fits 32 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 47.1 GB | ✗ No | — | — |
| Q8_0 | 26.3 GB | ✓ Yes | 32K | — |
| Q6_K | 20.9 GB | ✓ Yes | 32K | — |
| Q5_K_M | 18.4 GB | ✓ Yes | 32K | — |
| Q4_K_M | 16.1 GB | ✓ Yes | 32K | — |
| Q3_K_M | 12.1 GB | ✓ Yes | 32K | — |
| Q2_K | 10 GB | ✓ Yes | 32K | — |
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 16.1 GB · 32K 21.7 GB. Every window this model publishes still fits 32 GB.
No compatible GPU? Codestral 22B on 16 GB of system RAM, CPU only: runs, but it is tight.
Codestral Sizes That Fit the NVIDIA GeForce RTX 5090 (at 8K context)
| Codestral 22B | Q4_K_M · 16.1 GB at 8K context · ~86 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
Can I run Codestral on the NVIDIA GeForce RTX 5090?
Yes, comfortably — Codestral 22B at Q4_K_M needs 16.1 GB at 8K context (13.4 GB weights + 2.7 GB KV cache/overhead), leaving ~15.9 GB of the NVIDIA GeForce RTX 5090's 32 GB, at ~86 tok/s (est.), with room for up to 32K context.
What's the best Codestral size for the NVIDIA GeForce RTX 5090?
Codestral 22B at Q4_K_M quantization needs 16.1 GB at 8K context (13.4 GB weights + 2.7 GB KV cache/overhead), estimated ~86 tokens/sec, up to 32K context.
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