Gemma 9B (INT4) on RTX 5080: Monthly Cost & Token Output
Dedicated RTX 5080 hosting for Gemma 9B (INT4) (9B INT4) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
137.5 tok/s from a quantised 9B model is impressive by any standard. The RTX 5080 excels at INT4 inference, and combined with Gemma 9B’s strong reasoning capabilities, you get 356 million tokens monthly for £109. That is serious throughput at a competitive price point.
| Metric | Value |
|---|---|
| GPU | RTX 5080 (16 GB VRAM) |
| Model | Gemma 9B (INT4) (9B INT4 parameters) |
| Monthly Server Cost | £109/mo |
| Tokens/Second | ~137.5 tok/s |
| Tokens/Day (24h) | ~11,880,000 |
| Tokens/Month | ~356,400,000 |
| Effective Cost per 1M Tokens | £0.3058 |
Speed and Efficiency Through Quantisation
The 5080’s architecture is optimised for quantised workloads. Here is the economic comparison:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5080) | £0.3058 | — |
| 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 arrives at approximately 545M tokens/month. The 5080’s optimised INT4 performance means it handles concurrent load with minimal throughput degradation, making break-even more achievable in practice.
Hardware & Configuration Notes
11 GB of free VRAM on a latest-gen GPU means excellent batching performance. The 5080’s higher memory bandwidth particularly benefits quantised inference, where throughput is often memory-bandwidth-limited.
- VRAM usage: Gemma 9B (INT4) requires approximately 5 GB VRAM. The RTX 5080 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. The 5080’s newer architecture further boosts quantised inference speed.
- 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 (INT4) on RTX 5080
- Fast quantised inference for production chatbots
- High-throughput document analysis pipelines
- Real-time reasoning tasks requiring quick responses
- Cost-optimised enterprise AI deployments
- Medium-to-high traffic API backends
356M Tokens at 137.5 tok/s — £109/Month
Run quantised Gemma 9B on a dedicated RTX 5080 for the best speed-to-cost ratio.
