LLaMA 3 70B (INT4) on RTX 5090: Monthly Cost & Token Output
Dedicated RTX 5090 hosting for LLaMA 3 70B (INT4) (70B INT4) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
Double the throughput of the 3090 variant, with triple the free VRAM. The RTX 5090 runs LLaMA 3 70B INT4 at 29.4 tok/s with 12 GB of headroom for KV cache and batching. At £179/month, you get 76 million tokens of monthly capacity — GPT-4-class quality without a single API call.
| Metric | Value |
|---|---|
| GPU | RTX 5090 (32 GB VRAM) |
| Model | LLaMA 3 70B (INT4) (70B INT4 parameters) |
| Monthly Server Cost | £179/mo |
| Tokens/Second | ~29.4 tok/s |
| Tokens/Day (24h) | ~2,540,160 |
| Tokens/Month | ~76,204,800 |
| Effective Cost per 1M Tokens | £2.3489 |
Premium Model, Premium GPU, Fixed Price
70B models compete with the best commercial APIs. Here is what self-hosting saves you:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5090) | £2.3489 | — |
| Together.ai | $0.88 | Comparable |
| Fireworks | $0.90 | Comparable |
| Groq | $0.59 | Comparable |
Break-Even Analysis
Compared to Groq at $0.59/1M tokens, break-even lands at approximately 303.4M tokens/month. The 12 GB of free VRAM enables meaningful batching that the 3090 variant cannot match, making the 5090 the better choice for any workload with concurrent users.
Hardware & Configuration Notes
12 GB of free VRAM is a significant upgrade over the 3090’s 4 GB. This enables multi-user serving, deeper KV caches for long-context tasks, and more comfortable concurrent operation.
- VRAM usage: LLaMA 3 70B (INT4) requires approximately 20 GB VRAM. The RTX 5090 provides 32 GB, leaving 12 GB headroom for KV cache and batching.
- Quantisation: INT4 quantisation reduces VRAM from 40 GB to ~20 GB. The 5090’s 32 GB leaves 12 GB free for KV cache and concurrent serving.
- 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 5090 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for LLaMA 3 70B (INT4) on RTX 5090
- Production deployment of GPT-4-class open-source AI
- Multi-user access to frontier-quality reasoning
- Complex document analysis requiring deep understanding
- Code generation and review with state-of-the-art quality
- High-stakes content where model quality is paramount
Frontier AI, £179/Month, No API Lock-In
Deploy LLaMA 3 70B INT4 on a dedicated RTX 5090. Maximum 70B performance on a single GPU.
