LLaMA 3 70B (INT4) on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for LLaMA 3 70B (INT4) (70B INT4) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
Running a 70B-parameter model on a single £89/month GPU sounds impossible — but INT4 quantisation makes it real. LLaMA 3 70B compresses to ~20 GB, fitting within the RTX 3090’s 24 GB VRAM. The trade-off is throughput: 14 tok/s is not fast, but for batch workloads and low-concurrency applications, it opens the door to GPT-4-class quality at a fraction of API pricing.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | LLaMA 3 70B (INT4) (70B INT4 parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~14.0 tok/s |
| Tokens/Day (24h) | ~1,209,600 |
| Tokens/Month | ~36,288,000 |
| Effective Cost per 1M Tokens | £2.4526 |
70B Quality on a Single Consumer GPU
LLaMA 3 70B competes with premium commercial models. Here is how the dedicated hardware cost compares to API providers:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £2.4526 | — |
| Together.ai | $0.88 | Comparable |
| Fireworks | $0.90 | Comparable |
| Groq | $0.59 | Comparable |
Break-Even Analysis
Against Groq at $0.59/1M tokens, break-even is approximately 150.8M tokens/month. That is roughly 4.2x the RTX 3090’s monthly single-stream capacity — but for teams processing batches overnight or serving a handful of concurrent users, the savings compound quickly.
Hardware & Configuration Notes
INT4 quantisation compresses LLaMA 3 70B from ~40 GB to ~20 GB, leaving 4 GB free on the 3090. This is a tight fit, so batch sizes will be limited. For higher concurrency, consider the RTX 5090 variant.
- VRAM usage: LLaMA 3 70B (INT4) requires approximately 20 GB VRAM. The RTX 3090 provides 24 GB, leaving 4 GB headroom for KV cache and batching.
- Quantisation: INT4 quantisation reduces VRAM from 40 GB to ~20 GB. Fits on a single 24 GB GPU with room for KV cache.
- 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 3090 nodes behind a load balancer. GigaGPU supports multi-server deployments with simple configuration.
Best Use Cases for LLaMA 3 70B (INT4) on RTX 3090
- Low-concurrency applications requiring GPT-4-class reasoning
- Overnight batch processing of complex documents
- Research and evaluation of large model outputs
- Internal analysis tools where latency is less critical
- Cost-sensitive production use with request queuing
70B Intelligence for £89/Month
Run LLaMA 3 70B INT4 on a dedicated RTX 3090. One GPU, one price, frontier-class capability.
