Phi-3 on RTX 4060 Ti: Monthly Cost & Token Output
Dedicated RTX 4060 Ti hosting for Phi-3 (3.8B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
272 million tokens per month for £69. The RTX 4060 Ti gives Phi-3 a generous 12 GB of spare VRAM, making this setup exceptional for high-concurrency deployments where many users share a single GPU. With 105 tok/s throughput, responses arrive fast enough for real-time interaction.
| Metric | Value |
|---|---|
| GPU | RTX 4060 Ti (16 GB VRAM) |
| Model | Phi-3 (3.8B parameters) |
| Monthly Server Cost | £69/mo |
| Tokens/Second | ~105.0 tok/s |
| Tokens/Day (24h) | ~9,072,000 |
| Tokens/Month | ~272,160,000 |
| Effective Cost per 1M Tokens | £0.2535 |
Dedicated Hosting Economics for Phi-3
Phi-3’s small size keeps API pricing low too, but dedicated hardware adds predictability and data control:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 4060 Ti) | £0.2535 | — |
| Together.ai | $0.10 | Comparable |
| Fireworks | $0.20 | Comparable |
| Azure OpenAI | $0.26 | 3% cheaper |
Break-Even Analysis
Against Together.ai at $0.10/1M tokens, the break-even is roughly 690M tokens/month. The 4060 Ti’s 12 GB of free VRAM enables vLLM to batch requests aggressively, pushing real-world throughput well above the single-stream 105 tok/s figure and making break-even more attainable than it appears.
Hardware & Configuration Notes
12 GB of free VRAM for a 3.8B model is unusually generous. This headroom translates directly into higher concurrent user capacity, deeper context windows, and the option to co-host a second small model.
- VRAM usage: Phi-3 requires approximately 4 GB VRAM. The RTX 4060 Ti provides 16 GB, leaving 12 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 4060 Ti nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Phi-3 on RTX 4060 Ti
- High-concurrency chatbots on budget hardware
- Multi-model deployments pairing Phi-3 with a larger model
- Rapid prototyping and A/B testing of model outputs
- Automated form filling and data entry assistance
- Classroom and educational AI assistants
272M Tokens, £69/Month, 12 GB Free VRAM
Deploy Phi-3 on a dedicated RTX 4060 Ti with room for concurrent users and secondary models.
