Phi-3 on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for Phi-3 (3.8B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
140 tokens per second is fast by any standard, and on a £89/month RTX 3090, Phi-3 absolutely flies. With 20 GB of spare VRAM, you could run Phi-3 alongside a 7B model on the same card and still have headroom. 362 million tokens of monthly capacity at just £0.25/1M makes this a powerful option for teams that need speed and flexibility.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | Phi-3 (3.8B parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~140.0 tok/s |
| Tokens/Day (24h) | ~12,096,000 |
| Tokens/Month | ~362,880,000 |
| Effective Cost per 1M Tokens | £0.2453 |
Cost Efficiency Meets Massive VRAM Headroom
Phi-3’s 3.8B parameters leave the RTX 3090’s 24 GB VRAM mostly empty. That spare capacity translates into production advantages:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £0.2453 | — |
| Together.ai | $0.10 | Comparable |
| Fireworks | $0.20 | Comparable |
| Azure OpenAI | $0.26 | 6% cheaper |
Break-Even Analysis
Compared to Together.ai at $0.10/1M tokens, the break-even is approximately 890M tokens/month. With 20 GB of free VRAM fueling aggressive batching, the 3090 can serve heavy concurrent workloads that push practical throughput significantly above the 140 tok/s baseline.
Hardware & Configuration Notes
20 GB of free VRAM opens up possibilities that go far beyond running a single model. Consider co-hosting Phi-3 for quick responses alongside a larger model for complex queries, all on one £89/month server.
- VRAM usage: Phi-3 requires approximately 4 GB VRAM. The RTX 3090 provides 24 GB, leaving 20 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 Phi-3 on RTX 3090
- Dual-model deployments combining speed and capability
- High-throughput chatbot backends with deep context
- Large-scale batch text processing and classification
- Research experimentation with rapid iteration cycles
- Production APIs serving diverse LLM workloads from a single GPU
362M Tokens/Month, 20 GB Spare VRAM
Deploy Phi-3 on an RTX 3090 for maximum flexibility. £89/month, flat rate, no limits.
