Can the RTX 3090 Run Qwen2.5 32B?
Runs
Yes. Qwen2.5 32B at Q4_K_M is a 19.9 GB download (Hugging Face file listing for bartowski/Qwen2.5-32B-Instruct-GGUF), and the RTX 3090 has 24 GB of VRAM, leaving about 4.1 GB for the KV cache and runtime. Published runs put generation at a median of 23 tokens per second across 2 cited measurements. Fastest cited run: Kunal Ganglani LLM Benchmarks.
Buy the RTX 3090
msi Gaming GeForce RTX 3090 24GB GDRR6X 384-Bit HDMI/DP 1875 MHz Ampere Architecture OC…Tracked at $1,760 on 2026-09-24 — price may vary.
Published runs for Qwen2.5 32B on the RTX 3090
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 28 tok/s | q4_K_M | llama.cpp | — | Kunal Ganglani LLM Benchmarkslabelled "qwen2.5:32b" | 2024-01-01 |
| 18 tok/s | q4_K_M | llama.cpp | — | Ajit Singh (singhajit.com)labelled "qwen2.5:32b" | 2026-01-15 |
More about this pair
How much VRAM does Qwen2.5 32B need?
The Q4_K_M file is 19.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 21.4 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What is the fastest Qwen2.5 32B can generate on the RTX 3090?
Generation reads every weight once per token, so the RTX 3090's 936 GB/s of memory bandwidth divided by the 19.9 GB file sets a ceiling near 47 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.
Check another pair
Qwen2.5 32B on other GPUs
- RTX 4060 — Offload
- RTX 3060 Ti — Offload
- RTX 3060 12GB — Offload
- Intel Arc B580 — Offload
- RTX 4070 SUPER — Offload
- RTX 4060 Ti 16GB — Offload
- RTX 5060 Ti 16GB — Offload
- Intel Arc A770 16GB — Offload
- Radeon RX 9070 XT — Offload
- RTX 4070 Ti SUPER — Offload
- RTX 4080 — Offload
- RTX 5070 Ti — Offload
- RTX 5080 — Offload
- Radeon RX 7900 XTX — Runs, 28 tok/s median
- RTX 4090 — Runs, 34.4 tok/s median
- RTX 5090 — Runs, 45.1 tok/s median
- RTX A6000 48GB — Runs, 26.1 tok/s median
- RTX PRO 6000 Blackwell 96GB — Runs
Other models on the RTX 3090
- Llama 3.2 1B — Runs, 267 tok/s median
- Llama 3.2 3B — Runs
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 92 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs, 101 tok/s median
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 55.8 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs, 37.5 tok/s median
- Qwen3 14B — Runs, 61.1 tok/s median
- Phi-4 14B — Runs
- gpt-oss-20b — Runs
- Gemma 3 27B — Runs
- Gemma 2 27B — Runs
- Qwen3 30B-A3B — Runs
- Qwen3 32B — Runs, 32.7 tok/s median
- DeepSeek-R1-Distill-Qwen 32B — Runs
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling above, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
As an Amazon Associate, SpecPicks earns from qualifying purchases.
This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.