LLaMA 3 8B on RTX 5090: Monthly Cost & Token Output
Dedicated RTX 5090 hosting for LLaMA 3 8B (8B) inference — fixed monthly pricing with unlimited tokens.
Half a Billion Tokens, One Fixed Price
The RTX 5090 is the fastest consumer GPU you can put LLaMA 3 8B on. At nearly 200 tokens per second, it churns through over 517 million tokens in a month. The bill? £179. Period. No overages, no throttling, no per-token arithmetic.
| Metric | Value |
|---|---|
| GPU | RTX 5090 (32 GB VRAM) |
| Model | LLaMA 3 8B (8B parameters) |
| Monthly Server Cost | £179/mo |
| Tokens/Second | ~199.5 tok/s |
| Tokens/Day (24h) | ~17,236,800 |
| Tokens/Month | ~517,104,000 |
| Effective Cost per 1M Tokens | £0.3462 |
Dedicated GPU vs. Metered APIs
Even budget-friendly API endpoints charge per token. When your monthly volume is measured in hundreds of millions, those fractions add up. Here is how the RTX 5090 compares:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5090) | £0.3462 | — |
| Together.ai | $0.18 | Comparable |
| Fireworks | $0.20 | Comparable |
| Groq | $0.05 | Comparable |
At full capacity on Together.ai, that same 517M tokens would cost roughly $93. On GigaGPU, it is £179 — but with 32 GB VRAM, zero rate limits, and total data privacy baked in.
Where Dedicated Wins on Pure Cost
Against Groq’s aggressively low $0.05/1M tokens, the break-even sits at approximately 3,580M tokens/month — well beyond single-stream capacity. However, the RTX 5090’s 32 GB VRAM enables large-batch concurrent inference that can push effective throughput far higher.
For teams that value predictable billing, full data control, and the ability to fine-tune or swap models at will, the dedicated server pays for itself in operational simplicity alone.
Configuration & Performance
- Massive VRAM headroom: LLaMA 3 8B needs just 8 GB, leaving 24 GB free for deep KV caches and large batch sizes.
- Quantisation optional: With this much VRAM, FP16 runs comfortably. INT8 can push throughput past 250 tok/s if needed.
- Multi-user serving: vLLM continuous batching can serve 50+ concurrent users from a single 5090 card.
- Cluster scaling: Stack multiple RTX 5090 servers for enterprise-grade throughput across thousands of concurrent requests.
Built For
- High-traffic production chatbots needing sub-50ms time-to-first-token
- Enterprise RAG systems with dozens of simultaneous users
- Real-time content generation at scale
- Parallel batch processing of millions of documents
- Multi-model deployments sharing a single GPU
Maximum Throughput, Flat Pricing
Deploy LLaMA 3 8B on a dedicated RTX 5090 — 200 tok/s, 32 GB VRAM, £399/month all-in.
