Qwen 7B on RTX 5090: Monthly Cost & Token Output
Dedicated RTX 5090 hosting for Qwen 7B (7B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
533 million tokens per month from a single card. The RTX 5090 runs Qwen 7B at over 205 tok/s, and its 32 GB VRAM leaves a massive 25 GB free for KV caches, concurrent users, or even a second model. At £179/month all-in, this is the ultimate Qwen 7B deployment for throughput-hungry teams.
| Metric | Value |
|---|---|
| GPU | RTX 5090 (32 GB VRAM) |
| Model | Qwen 7B (7B parameters) |
| Monthly Server Cost | £179/mo |
| Tokens/Second | ~205.8 tok/s |
| Tokens/Day (24h) | ~17,781,120 |
| Tokens/Month | ~533,433,600 |
| Effective Cost per 1M Tokens | £0.3356 |
Maximum Throughput, Predictable Billing
When volume is measured in hundreds of millions of tokens, the economics of dedicated hardware become compelling:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5090) | £0.3356 | — |
| Together.ai | $0.20 | Comparable |
| Fireworks | $0.20 | Comparable |
| DeepInfra | $0.13 | Comparable |
Break-Even Analysis
Against DeepInfra at $0.13/1M tokens, break-even sits at approximately 1,376.9M tokens/month. While that exceeds single-stream capacity, the 5090’s 25 GB of free VRAM enables deep batching that can push practical throughput far higher. For maximum-utilisation workloads, the savings are substantial.
Hardware & Configuration Notes
25 GB of spare VRAM means you can run the deepest possible KV caches, serve the highest concurrent user counts, and even co-host auxiliary models — all on a single card.
- VRAM usage: Qwen 7B requires approximately 7 GB VRAM. The RTX 5090 provides 32 GB, leaving 25 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 Qwen 7B on RTX 5090
- Enterprise-scale multilingual chatbot platforms
- Multi-model inference combining Qwen 7B with embedding models
- High-traffic API backends serving global user bases
- Massive batch processing of multilingual document corpora
- Research workloads requiring rapid iteration on model outputs
Peak Qwen 7B Performance: £179/Month
Deploy on a dedicated RTX 5090. 206 tok/s, 32 GB VRAM, flat-rate billing.
