Can I run Mistral 7B v0.3 on NVIDIA RTX 5090 32GB?
Yes: Mistral 7B v0.3 runs on NVIDIA RTX 5090 32GB with F16 at roughly 75 tokens/s. Estimated memory 15.3 GiB at 8,192 tokens of context.
With F16 you can push the context to about 32,768 tokens before memory runs out (F16 KV cache).
Memory by quantisation (8,192-token context)
| Quantisation | Memory | Verdict | Est. speed |
|---|---|---|---|
| F16Unquantized reference | 15.3 GiB | Fits | ~75 tok/s |
| Q8_0Near-lossless | 9.0 GiB | Fits | ~133 tok/s |
| Q6_KVery close to F16 | 7.3 GiB | Fits | ~166 tok/s |
| Q5_K_MGreat quality/size | 6.6 GiB | Fits | ~187 tok/s |
| Q4_K_MBest default for most | 5.9 GiB | Fits | ~213 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 5.4 GiB | Fits | ~236 tok/s |
| Q3_K_MNoticeable loss | 5.1 GiB | Fits | ~252 tok/s |
| Q2_KLast resort; big loss | 4.6 GiB | Fits | ~283 tok/s |
NVIDIA RTX 5090 32GB offers about 32 GiB of memory (94% usable for a model) at 1792 GB/s. Speed is a bandwidth-bound estimate of single-stream generation at 65% efficiency; real results vary by runtime, drivers and settings, so measure on your machine.
How this is calculated
Weights ≈ parameters × bits per weight ÷ 8. KV cache ≈ 2 × 32 layers × 8 KV heads × 128 head dim × 2 bytes per token. Decode speed ≈ efficiency × memory bandwidth ÷ bytes read per token. Learn the maths in VRAM math and memory bandwidth, or try the VRAM calculator.
Mistral 7B v0.3 on other hardware
Similar models on NVIDIA RTX 5090 32GB
Running slowly or out of memory? See CUDA out of memory and the quantisation lesson.