Phi-3 on RTX 5080: Monthly Cost & Token Output
Dedicated RTX 5080 hosting for Phi-3 (3.8B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
175 tokens per second. At that speed, a typical 300-token chatbot response appears in under two seconds. The RTX 5080 pushes Phi-3 to its limits, delivering 453 million tokens monthly at an effective rate of just £0.24 per million. For a model this small, the throughput-to-cost ratio is remarkable.
| Metric | Value |
|---|---|
| GPU | RTX 5080 (16 GB VRAM) |
| Model | Phi-3 (3.8B parameters) |
| Monthly Server Cost | £109/mo |
| Tokens/Second | ~175.0 tok/s |
| Tokens/Day (24h) | ~15,120,000 |
| Tokens/Month | ~453,600,000 |
| Effective Cost per 1M Tokens | £0.2403 |
Extreme Throughput for a Compact Model
Phi-3’s efficiency shines brightest on latest-generation hardware. Here is how the economics compare to metered API services:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5080) | £0.2403 | — |
| Together.ai | $0.10 | Comparable |
| Fireworks | $0.20 | Comparable |
| Azure OpenAI | $0.26 | 8% cheaper |
Break-Even Analysis
Against Together.ai at $0.10/1M tokens, break-even arrives at approximately 1,090M tokens/month. While that exceeds single-stream capacity, the 5080’s 12 GB of free VRAM supports deep concurrent batching that can push practical throughput considerably higher under production load.
Hardware & Configuration Notes
Phi-3 uses just 4 GB of the 5080’s 16 GB VRAM. The 12 GB of headroom supports massive concurrent serving or multi-model deployments.
- VRAM usage: Phi-3 requires approximately 4 GB VRAM. The RTX 5080 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 5080 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for Phi-3 on RTX 5080
- Ultra-low-latency conversational interfaces
- Real-time content suggestion and auto-completion
- High-frequency API backends for lightweight LLM tasks
- Embedded AI features in SaaS products
- Parallel processing of thousands of short-form queries
175 tok/s Phi-3 — £109/Month
Deploy on a dedicated RTX 5080 for the fastest compact-model inference available.
