Gemma 9B on RTX 5090: Monthly Cost & Token Output
Dedicated RTX 5090 hosting for Gemma 9B (9B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
462 million tokens monthly and 23 GB of spare VRAM. The RTX 5090 gives Gemma 9B room for massive concurrent batching and even co-hosting auxiliary models. At £179/month and 178.5 tok/s, this is the premium Gemma 9B setup for teams that need maximum throughput and flexibility.
| Metric | Value |
|---|---|
| GPU | RTX 5090 (32 GB VRAM) |
| Model | Gemma 9B (9B parameters) |
| Monthly Server Cost | £179/mo |
| Tokens/Second | ~178.5 tok/s |
| Tokens/Day (24h) | ~15,422,400 |
| Tokens/Month | ~462,672,000 |
| Effective Cost per 1M Tokens | £0.3869 |
Premium Throughput, Fixed Cost
The 5090’s 32 GB VRAM makes it the ideal home for Gemma 9B at scale. Here is how it compares to API pricing:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5090) | £0.3869 | — |
| 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 895M tokens/month. With 23 GB of free VRAM enabling deep batching, the 5090 can serve extremely high concurrent loads. For enterprise deployments, the economics become very favourable.
Hardware & Configuration Notes
23 GB of free VRAM means you can run Gemma 9B alongside embedding models, secondary inference models, or any other GPU-accelerated workload — all on a single £179/month card.
- VRAM usage: Gemma 9B requires approximately 9 GB VRAM. The RTX 5090 provides 32 GB, leaving 23 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 5090 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Gemma 9B on RTX 5090
- Enterprise-scale Gemma 9B deployments
- Multi-model setups combining reasoning and retrieval
- High-concurrency production chatbot platforms
- Large-scale document processing and analysis
- Research workloads requiring maximum model throughput
Peak Gemma 9B — £399/Month
Maximise throughput with a dedicated RTX 5090. 32 GB VRAM, flat-rate billing, zero limits.
