Gemma 9B on RTX 4060 Ti: Monthly Cost & Token Output
Dedicated RTX 4060 Ti hosting for Gemma 9B (9B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
Google’s Gemma 9B brings solid reasoning capability in a 9-billion-parameter package. On a dedicated RTX 4060 Ti at £69/month, it generates roughly 165 million tokens monthly at 63.8 tok/s. The 16 GB of VRAM gives it 7 GB of headroom for KV cache after loading the model.
| Metric | Value |
|---|---|
| GPU | RTX 4060 Ti (16 GB VRAM) |
| Model | Gemma 9B (9B parameters) |
| Monthly Server Cost | £69/mo |
| Tokens/Second | ~63.8 tok/s |
| Tokens/Day (24h) | ~5,512,320 |
| Tokens/Month | ~165,369,600 |
| Effective Cost per 1M Tokens | £0.4172 |
Dedicated Hosting vs. API Pricing for Gemma 9B
Gemma 9B is available through Google and third-party providers. Here is how dedicated hardware compares:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 4060 Ti) | £0.4172 | — |
| 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, the break-even is approximately 345M tokens/month. Above that volume, dedicated hardware delivers pure savings. The 4060 Ti’s 7 GB of spare VRAM supports decent batching for moderate-concurrency workloads.
Hardware & Configuration Notes
Gemma 9B is slightly larger than typical 7B models, consuming ~9 GB VRAM. The 4060 Ti’s 16 GB leaves 7 GB free, which is adequate for small to medium batch sizes.
- VRAM usage: Gemma 9B requires approximately 9 GB VRAM. The RTX 4060 Ti 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 4060 Ti nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Gemma 9B on RTX 4060 Ti
- Reasoning-focused chatbot applications
- Academic and research question-answering systems
- Content quality assessment and editorial review
- Structured data extraction from unstructured text
- Knowledge synthesis across multiple documents
Gemma 9B from £69/Month
Deploy Google’s 9B model on a dedicated RTX 4060 Ti. Fixed pricing, unlimited tokens, full control.
