Qwen 7B on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for Qwen 7B (7B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
The RTX 3090 offers the best value-per-VRAM ratio on GigaGPU for Qwen 7B. 24 GB of VRAM means only 7 GB goes to the model and the remaining 17 GB can power deep context windows and aggressive batching. At £89/month and ~98 tok/s, you get 254 million tokens of monthly capacity.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | Qwen 7B (7B parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~98.0 tok/s |
| Tokens/Day (24h) | ~8,467,200 |
| Tokens/Month | ~254,016,000 |
| Effective Cost per 1M Tokens | £0.3504 |
Dedicated Hardware vs. API Bills
With 17 GB of spare VRAM enabling real-world throughput that often exceeds single-stream benchmarks, the cost dynamics shift in favour of dedicated hardware:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £0.3504 | — |
| 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 is approximately 684.6M tokens/month. The RTX 3090’s 17 GB of free VRAM allows vLLM to batch aggressively, pushing practical throughput toward and sometimes past the break-even threshold for busy production workloads.
Hardware & Configuration Notes
17 GB of headroom is generous for a 7B model. This enables deep KV caches for long context windows, large batch sizes for high-concurrency serving, or even hosting an auxiliary embedding model alongside Qwen 7B on the same card.
- VRAM usage: Qwen 7B requires approximately 7 GB VRAM. The RTX 3090 provides 24 GB, leaving 17 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 Qwen 7B on RTX 3090
- High-volume multilingual chatbot platforms
- Document-level translation and summarisation
- RAG systems serving multiple concurrent users
- Automated content generation in multiple languages
- Large-scale text mining and information extraction
24 GB VRAM, £89/Month, Unlimited Tokens
Deploy Qwen 7B on a dedicated RTX 3090. No per-token fees, no rate limits, full root access.
