gemma3-12b vs qwen2.5-72b on RTX 4090
Compare gemma3-12b and qwen2.5-72b running locally on RTX 4090 (24GB VRAM) — tokens/sec, VRAM fit, and quality scores for local inference.
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| Quant | Q4_K_M |
| VRAM required | 7.5GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 61 / 100 |
| Eval sources | MMLU-Pro · IFEval · HumanEval · MBPP+ · LiveCodeBench |
| Data | Community benchmark |
| Quant | Q4_K_M |
| VRAM required | 43.5GB |
| Fits on RTX 4090 | ✗ No |
| Quality score | 72 / 100 |
| Eval sources | MMLU-Pro · IFEval · BBH · LiveCodeBench |
| Data | estimate |
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Which is faster on RTX 4090: gemma3-12b or qwen2.5-72b?
This RTX 4090 comparison shows VRAM fit and quality data for gemma3-12b vs qwen2.5-72b, but one or both decode-speed benchmarks are still missing.
Do both models fit on RTX 4090?
gemma3-12b: ✓. qwen2.5-72b: ✗ No. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
gemma3-12b needs about 7.5GB in Q4_K_M; qwen2.5-72b needs about 43.5GB in Q4_K_M.
Why can speed differ by GPU?
Local LLM speed depends on GPU memory bandwidth, backend, quantization, context length, and whether the model fits entirely in VRAM or unified memory.
How can I correct this comparison?
Use the GitHub correction link on this page with your GPU, model, quantization, context length, and measured decode tokens per second.