Gemma 9B on RTX 5080: Monthly Cost & Token Output
Dedicated RTX 5080 hosting for Gemma 9B (9B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
The RTX 5080 pushes Gemma 9B past the 100 tok/s mark, delivering 275 million tokens monthly at £109. For applications where response speed directly impacts user experience, the 25% throughput improvement over the RTX 3090 is worth every penny of the £20 price difference.
| Metric | Value |
|---|---|
| GPU | RTX 5080 (16 GB VRAM) |
| Model | Gemma 9B (9B parameters) |
| Monthly Server Cost | £109/mo |
| Tokens/Second | ~106.2 tok/s |
| Tokens/Day (24h) | ~9,175,680 |
| Tokens/Month | ~275,270,400 |
| Effective Cost per 1M Tokens | £0.396 |
Latest-Gen Performance for Gemma 9B
The 5080’s newer architecture provides meaningful speed gains for 9B-class models. Here is the economic picture:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5080) | £0.396 | — |
| Together.ai | $0.20 | Comparable |
| Fireworks | $0.20 | Comparable |
| Google Vertex | $0.30 | Comparable |
Break-Even Analysis
Against Together.ai at $0.20/1M tokens, break-even is approximately 545M tokens/month. The 5080’s higher memory bandwidth translates to better performance under concurrent load, helping close the gap between theoretical break-even and real-world savings.
Hardware & Configuration Notes
Gemma 9B occupies ~9 GB of the 5080’s 16 GB VRAM, leaving 7 GB free. While tighter than the 3090, the newer architecture compensates with higher throughput per unit of VRAM.
- VRAM usage: Gemma 9B requires approximately 9 GB VRAM. The RTX 5080 provides 16 GB, leaving 7 GB headroom for KV cache and batching.
- Quantisation: Running in FP16 by default. INT8 or INT4 quantisation can reduce VRAM usage and increase throughput by 20–40% with minimal quality loss for most use cases.
- Batching: With continuous batching enabled (e.g., vLLM or TGI), you can serve multiple concurrent users from a single GPU, increasing effective throughput significantly.
- Scaling: Need more throughput? Add additional RTX 5080 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Gemma 9B on RTX 5080
- Speed-sensitive reasoning and analysis applications
- Real-time educational tutoring systems
- Interactive document review and annotation
- Latency-critical API backends for Gemma-powered features
- Production chatbots requiring fast multi-turn responses
106 tok/s Gemma 9B — £109/Month
Deploy on a dedicated RTX 5080 for fast, flat-rate Gemma 9B inference.
