Can I Run Codestral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated May 29, 2024
Yes
Yes — Codestral 22B at Q8_0 needs about 26.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~5.7 GB spare), at ~48.8 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~48.8 tok/s
See what else this hardware can run →
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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Codestral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 47.1 GB | ✗ No | — | — | 44.4 GB |
| Q8_0 | 26.3 GB | ✓ Yes | 32K | ~48.8 tok/s | 23.6 GB |
| Q6_K | 20.9 GB | ✓ Yes | 32K | ~60.6 tok/s | 18.2 GB |
| Q5_K_M | 18.4 GB | ✓ Yes | 32K | ~68.2 tok/s | 15.7 GB |
| Q4_K_M | 16.1 GB | ✓ Yes | 32K | ~77.4 tok/s | 13.4 GB |
| Q3_K_M | 12.1 GB | ✓ Yes | 32K | ~100.1 tok/s | 9.5 GB |
| Q2_K | 10 GB | ✓ Yes | 32K | ~119.4 tok/s | 7.3 GB |
RTX 5090 desktop limitations
- 575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
- Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Codestral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Yes — Codestral 22B at Q8_0 needs about 26.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~5.7 GB spare), at ~48.8 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Codestral should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 26.3 GB of the 32 GB available, downloads as roughly 23.6 GB, and runs at an estimated 48.8 tokens/sec with up to 32K of context.
What limits Codestral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Cogito v1 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Command R Family on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Cosmos 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- DeepSeek-OCR on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- DeepSeek R1 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
Codestral on GPUs
- Codestral on NVIDIA GeForce RTX 5090
- Codestral on NVIDIA GeForce RTX 5080
- Codestral on NVIDIA GeForce RTX 5070 Ti
- Codestral on NVIDIA GeForce RTX 5070
What This Model Is Good At
Model & Tools
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