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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.

Model file19.9 GBQ4_K_M
VRAM24 GBRTX 3090
Headroom4.1 GBafter weights
Median speed23 tok/s2 cited runs
Bandwidth ceiling~47 tok/s936 GB/s ÷ 19.85 GB

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Published runs for Qwen2.5 32B on the RTX 3090

GenerationQuantRuntimeContextSourceDate
28 tok/s q4_K_M llama.cpp — Kunal Ganglani LLM Benchmarkslabelled "qwen2.5:32b" date not given
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 speed does Qwen2.5 32B reach on the RTX 3090?

The fastest published run is 28 tokens per second (Kunal Ganglani LLM Benchmarks, q4_K_M, llama.cpp); the median of 2 cited runs is 23 tok/s. Generation reads every weight once per token, so the RTX 3090's 936 GB/s of memory bandwidth divided by the 19.85 GB file puts the theoretical ceiling near 47 tokens per second. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it, and SpecPicks rejects any published figure above it.

Check another pair

See the full matrix → · All RTX 3090 benchmarks →

Qwen2.5 32B on other GPUs

Other models on the RTX 3090

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 (memory bandwidth ÷ GB read per token: the whole file for a dense model, only the active experts' weights for a mixture-of-experts model such as gpt-oss-20b or Qwen3 30B-A3B, which is why those can post figures above bandwidth ÷ file size), it is not slower than the same card on a model twice its size, a single run is not slower than the same card on a larger dense model, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.

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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.