LLaMA 3 8B on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for LLaMA 3 8B (8B) inference — fixed monthly pricing with unlimited tokens.
246 Million Tokens for £89/Month
The RTX 3090 remains one of the best value propositions in GPU inference. Running LLaMA 3 8B at ~95 tokens per second, it delivers roughly 246 million tokens every month — enough to power a busy production chatbot or process entire document libraries overnight.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | LLaMA 3 8B (8B parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~95.0 tok/s |
| Tokens/Day (24h) | ~8,208,000 |
| Tokens/Month | ~246,240,000 |
| Effective Cost per 1M Tokens | £0.3614 |
Self-Hosted vs. Per-Token APIs
With 24 GB of VRAM, the RTX 3090 has plenty of room for LLaMA 3 8B plus a large KV cache. Compared to metered API providers:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £0.3614 | — |
| Together.ai | $0.18 | Comparable |
| Fireworks | $0.20 | Comparable |
| Groq | $0.05 | Comparable |
API per-token rates look attractive until you multiply them by monthly volume. At 246M tokens, a Fireworks bill would run to $49.20 — comparable to GigaGPU, but without the data sovereignty and unlimited-use ceiling you get with dedicated hardware.
Break-Even Calculation
Against Groq’s $0.05/1M rate, the RTX 3090 breaks even around 1,780M tokens/month. That sounds high, but remember: with continuous batching enabled, actual throughput under concurrent load can exceed the single-stream figure substantially.
Teams already spending £89 or more per month on API calls should run the numbers — dedicated hardware often wins on both cost and control.
Why the RTX 3090 Excels Here
- 16 GB headroom: LLaMA 3 8B uses ~8 GB VRAM, leaving 16 GB free for KV cache, batched sequences, and concurrent request handling.
- Quantisation upside: INT8 or INT4 quantisation can push throughput 20–40% higher while preserving output quality for production use.
- Continuous batching: Pair with vLLM or TGI to serve dozens of simultaneous users from a single GPU.
- Multi-node ready: Add more RTX 3090 servers behind a load balancer when one card is no longer enough.
Where This Setup Shines
- High-volume customer service and internal helpdesk bots
- Automated content generation at scale
- Multi-user RAG deployments
- Code-assist and pair-programming tools
- Nightly batch jobs on large text datasets
Deploy LLaMA 3 8B on an RTX 3090
Get 24 GB VRAM, 95 tok/s throughput, and unlimited tokens for £89/month. Server ships pre-configured for inference.
