run it locally
Run Gemma 2 27B on RTX 3090
27.2B parameters on a 24 GB card. It fits at Q5_K_M — here's the VRAM math at every quantization, the speed to expect, and the quality you trade.
| quantization | vram needed | fits 24gb? | tokens/sec | quality |
|---|---|---|---|---|
| FP16 (full) | 65.3 GB | ✗ no | — | Reference quality — no quantization loss. |
| Q8_0 | 34.6 GB | ✗ no | — | Near-lossless; rarely worth the extra space over Q6. |
| Q6_K | 26.8 GB | ✗ no | — | Virtually indistinguishable from full precision. |
| Q5_K_Mrecommended | 22.5 GB | ✓ yes | ~42 | Minor loss; an excellent quality-vs-size balance. |
| Q4_K_M | 18.3 GB | ✓ yes | ~52 | Small but measurable loss; the popular default. |
| Q3_K_M | 14.0 GB | ✓ yes | ~68 | Noticeable degradation; only when you're tight on VRAM. |
Weights = params × bytes/weight, +20% for KV cache & runtime; usable VRAM is 95% of nameplate. Tokens/sec is a bandwidth ceiling (936 GB/s) — real throughput is lower with long context. Try other combinations →
Running Gemma 2 27B on a RTX 3090
At Q5_K_M, Gemma 2 27B's weights take about 23 GB, inside the RTX 3090's 23 GB of usable memory. Minor loss; an excellent quality-vs-size balance. Generation speed is bound by memory bandwidth — the GPU reads the whole model once per token — so expect on the order of 42 tokens/sec before context overhead.
Quantization is the lever
Each step down in precision shrinks the model: FP16 needs about 65 GB, Q4_K_M about 18 GB — a 72% reduction for a small, usually acceptable quality cost. Q5_K_M and Q6_K are near-lossless if you have the headroom; drop to Q3 only when you're genuinely out of VRAM. The quantization guide covers the tradeoffs in detail.
Frequently asked questions
Can a RTX 3090 run Gemma 2 27B?
Yes — Gemma 2 27B fits on a RTX 3090 at Q5_K_M (23 GB of the 23 GB usable), with roughly 42 tokens/sec. Higher-precision quants need more VRAM; the table above shows each option.
How much VRAM does Gemma 2 27B need?
Gemma 2 27B is a 27.2B-parameter model. At FP16 that's about 65 GB; at Q4_K_M (the popular default) about 18 GB, including ~20% for the KV cache and runtime. Quantization is the main lever — see the per-quant table above.
Other combinations
Gemma 2 27B on other GPUs: RTX 3060 12GB, RTX 4070 Ti, RTX 4080, RTX 4090, NVIDIA L4, RTX A6000
Other models on the RTX 3090: Gemma 2 9B, Phi-3 Medium 14B, Qwen2.5 32B, Command R 35B, Mixtral 8x7B, Llama 3.3 70B
Related
- VRAM calculator — any model, quant and GPU.
- Running models locally — the hardware reality.
- All run-locally combinations →