Can I run Qwen2.5 32B on NVIDIA RTX 5090 32GB?
Yes: Qwen2.5 32B runs on NVIDIA RTX 5090 32GB with Q5_K_M at roughly 46 tokens/s. Estimated memory 25.0 GiB at 8,192 tokens of context.
With Q5_K_M you can push the context to about 15,872 tokens before memory runs out (F16 KV cache).
Memory by quantisation (8,192-token context)
| Quantisation | Memory | Verdict | Est. speed |
|---|---|---|---|
| F16Unquantized reference | 64.3 GiB | Needs CPU offload | — |
| Q8_0Near-lossless | 35.7 GiB | Needs CPU offload | — |
| Q6_KVery close to F16 | 28.3 GiB | Tight | ~40 tok/s |
| Q5_K_MGreat quality/size | 25.0 GiB | Fits | ~46 tok/s |
| Q4_K_MBest default for most | 21.8 GiB | Fits | ~53 tok/s |
| IQ4_XSSmaller 4-bit (imatrix) | 19.5 GiB | Fits | ~60 tok/s |
| Q3_K_MNoticeable loss | 18.2 GiB | Fits | ~64 tok/s |
| Q2_KLast resort; big loss | 16.1 GiB | Fits | ~73 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 × 64 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.
Qwen2.5 32B 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.