Gemma 9B on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for Gemma 9B (9B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
With 15 GB of free VRAM after loading Gemma 9B, the RTX 3090 offers generous headroom for concurrent serving. At 85 tok/s and £89/month, you get 220 million tokens of monthly capacity — more than enough for a production chatbot or document processing pipeline.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | Gemma 9B (9B parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~85.0 tok/s |
| Tokens/Day (24h) | ~7,344,000 |
| Tokens/Month | ~220,320,000 |
| Effective Cost per 1M Tokens | £0.404 |
Why Self-Hosting Gemma 9B Makes Sense
The RTX 3090’s 24 GB VRAM makes it a natural home for 9B-class models. Here is the cost comparison:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £0.404 | — |
| Together.ai | $0.20 | Comparable |
| Fireworks | $0.20 | Comparable |
| Google Vertex | $0.30 | Comparable |
Break-Even Analysis
Compared to Together.ai at $0.20/1M tokens, break-even lands at roughly 445M tokens/month. The 3090’s 15 GB of free VRAM supports aggressive batching that can push practical throughput well above the 85 tok/s baseline under concurrent load.
Hardware & Configuration Notes
15 GB of spare VRAM is more than enough for deep KV caches and large batch sizes, making the RTX 3090 a strong mid-range choice for Gemma 9B production deployments.
- VRAM usage: Gemma 9B requires approximately 9 GB VRAM. The RTX 3090 provides 24 GB, leaving 15 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 3090 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Gemma 9B on RTX 3090
- Multi-turn reasoning and analysis chatbots
- Document review and compliance checking
- Enterprise Q&A systems with deep context windows
- Content generation requiring strong coherence
- Research and experimentation with Google’s model family
220M Tokens/Month, £89 Flat
Run Gemma 9B on a dedicated RTX 3090. 24 GB VRAM, zero per-token fees.
