Gemma 9B (INT4) on RTX 4060 Ti: Monthly Cost & Token Output
Dedicated RTX 4060 Ti hosting for Gemma 9B (INT4) (9B INT4) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
With INT4 quantisation, Gemma 9B occupies just 5 GB on the 4060 Ti’s 16 GB VRAM, leaving a generous 11 GB free. That headroom translates directly into higher concurrent user capacity. At 82.5 tok/s and £69/month, you get 213 million tokens of monthly throughput.
| Metric | Value |
|---|---|
| GPU | RTX 4060 Ti (16 GB VRAM) |
| Model | Gemma 9B (INT4) (9B INT4 parameters) |
| Monthly Server Cost | £69/mo |
| Tokens/Second | ~82.5 tok/s |
| Tokens/Day (24h) | ~7,128,000 |
| Tokens/Month | ~213,840,000 |
| Effective Cost per 1M Tokens | £0.3227 |
Quantised Gemma 9B: More Headroom, Lower Cost
Running quantised means lower VRAM usage and more room for batching. The per-token economics remain competitive:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 4060 Ti) | £0.3227 | — |
| 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 is approximately 345M tokens/month. The 11 GB of free VRAM enables vLLM to batch aggressively, pushing real-world throughput well above the single-stream 82.5 tok/s figure.
Hardware & Configuration Notes
11 GB of free VRAM for a quantised 9B model is excellent. This setup supports high-concurrency serving and could even accommodate a small secondary model alongside Gemma 9B.
- VRAM usage: Gemma 9B (INT4) requires approximately 5 GB VRAM. The RTX 4060 Ti provides 16 GB, leaving 11 GB headroom for KV cache and batching.
- Quantisation: INT4 quantisation reduces Gemma 9B from ~9 GB to ~5 GB VRAM, leaving 11 GB free on the 4060 Ti for generous KV cache and batching.
- 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 (INT4) on RTX 4060 Ti
- Multi-user chatbot deployments with deep batching
- Concurrent document processing for business teams
- RAG applications with generous context windows
- Quality-sensitive workloads that benefit from 9B-class reasoning
- Cost-optimised production inference
213M Tokens, 11 GB Free VRAM, £69/Month
Deploy quantised Gemma 9B on a dedicated RTX 4060 Ti for the best balance of cost and capability.
