deepseek-r1-7b vs gemma3-4b on RTX 4090
Compare deepseek-r1-7b and gemma3-4b 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 | 4.7GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 93 / 100 |
| Eval sources | GPQA Diamond · MATH-500 · AIME 2024 · Codeforces · LiveCodeBench |
| Data | estimate |
| Quant | Q4_K_M |
| VRAM required | 3GB |
| Fits on RTX 4090 | ✓ |
| Quality score | 44 / 100 |
| Eval sources | MMLU-Pro · IFEval · HumanEval · MBPP+ · LiveCodeBench |
| Data | Community benchmark |
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Which is faster on RTX 4090: deepseek-r1-7b or gemma3-4b?
gemma3-4b is faster on RTX 4090 in this dataset (190 vs 145 tok/s decode).
Do both models fit on RTX 4090?
deepseek-r1-7b: ✓. gemma3-4b: ✓. The fit labels use Q4_K_M VRAM estimates and the benchmark data available for this GPU.
How much VRAM do these models need?
deepseek-r1-7b needs about 4.7GB in Q4_K_M; gemma3-4b needs about 3GB 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.