Table of Contents
Gemma 2 9B Benchmark Overview
Google’s Gemma 2 9B is a 9-billion-parameter open model that delivers strong performance on reasoning, summarisation, and multilingual tasks. With roughly 18 GB of VRAM required at FP16, it sits at a sweet spot where dedicated GPU servers with mid-range cards can still run it comfortably. We benchmark inference speed across six GPUs to help you choose the right hardware.
Testing was conducted on GigaGPU dedicated servers using vLLM with a 512-token input prompt and 256-token output. Gemma 2 9B at FP16 requires approximately 18 GB of VRAM, so cards with 16 GB need quantisation. For full methodology details, see our tokens per second benchmark hub.
Tokens/sec Results by GPU
| GPU | VRAM | Gemma 2 9B FP16 (tok/s) | Notes |
|---|---|---|---|
| RTX 3050 | 6 GB | N/A | Insufficient VRAM |
| RTX 4060 | 8 GB | N/A | Insufficient VRAM for FP16 |
| RTX 4060 Ti | 16 GB | N/A | Tight fit; unstable at FP16 |
| RTX 3090 | 24 GB | 36 tok/s | Comfortable fit |
| RTX 5080 | 16 GB | N/A | Needs INT8 or INT4 |
| RTX 5090 | 32 GB | 78 tok/s | Plenty of headroom |
At FP16, only the RTX 3090 and RTX 5090 have enough VRAM to run Gemma 2 9B reliably. For 16 GB cards, quantisation is essential. Check whether your target GPU is viable with our Can RTX 5080 run Gemma? guide.
Quantisation Comparison
Quantisation opens Gemma 2 9B to a wider range of GPUs. Below we compare FP16, INT8, and INT4 performance. For a detailed analysis of how quantisation affects quality and speed, see our FP16 vs INT8 vs INT4 comparison.
| GPU | FP16 (tok/s) | INT8 (tok/s) | INT4 (tok/s) |
|---|---|---|---|
| RTX 3050 (6 GB) | N/A | N/A | 7 tok/s |
| RTX 4060 (8 GB) | N/A | 14 tok/s | 18 tok/s |
| RTX 4060 Ti (16 GB) | N/A | 26 tok/s | 34 tok/s |
| RTX 3090 (24 GB) | 36 | 44 tok/s | 52 tok/s |
| RTX 5080 (16 GB) | N/A | 52 tok/s | 65 tok/s |
| RTX 5090 (32 GB) | 78 | 92 tok/s | 108 tok/s |
INT4 quantisation makes the RTX 4060 Ti a viable option at 34 tok/s, and even the RTX 4060 can manage 18 tok/s. The RTX 5080 at INT4 delivers 65 tok/s, making it competitive with the RTX 3090 at FP16.
Cost Efficiency Analysis
Comparing cost efficiency using INT4 performance, which is the practical configuration for most GPUs in this range. Prices are based on GigaGPU dedicated hosting rates.
| GPU | INT4 tok/s | Approx. Monthly Cost | tok/s per Pound |
|---|---|---|---|
| RTX 4060 | 18 | ~£60 | 0.30 |
| RTX 4060 Ti | 34 | ~£75 | 0.45 |
| RTX 3090 | 52 | ~£110 | 0.47 |
| RTX 5080 | 65 | ~£160 | 0.41 |
| RTX 5090 | 108 | ~£250 | 0.43 |
The RTX 3090 offers the best value per pound at INT4, followed closely by the RTX 4060 Ti. For a thorough comparison, see our best GPU for Gemma guide.
GPU Recommendations
- Best budget: RTX 4060 Ti at INT4 — 34 tok/s for ~£75/month is solid for development APIs.
- Best value: RTX 3090 — strong FP16 performance and top cost efficiency at INT4.
- Best performance: RTX 5090 — 78 tok/s FP16 or 108 tok/s INT4 for production workloads.
Compare these results with the Gemma 2 27B benchmark for the larger variant, or check the Phi-3 Mini results for a smaller alternative. All data is available in the Benchmarks category.
Conclusion
Gemma 2 9B offers a compelling balance of capability and size. With INT4 quantisation, it runs well on mid-range GPUs, while the RTX 3090 and RTX 5090 can handle it at full FP16 precision. Whether you are building a summarisation pipeline or a multilingual chatbot, Gemma 2 9B is a practical choice for dedicated GPU deployments.
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