LLaMA 3 70B (GPTQ) on RTX 3090: Monthly Cost & Token Output
Dedicated RTX 3090 hosting for LLaMA 3 70B (GPTQ) (70B GPTQ) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
GPTQ quantisation offers a quality-focused alternative to INT4 for compressing LLaMA 3 70B to a single GPU. At 12 tok/s on the RTX 3090, throughput is modest — but for applications where response quality matters more than speed, GPTQ’s slightly better perplexity scores can be worth the trade-off. The monthly cost? Just £89.
| Metric | Value |
|---|---|
| GPU | RTX 3090 (24 GB VRAM) |
| Model | LLaMA 3 70B (GPTQ) (70B GPTQ parameters) |
| Monthly Server Cost | £89/mo |
| Tokens/Second | ~12.0 tok/s |
| Tokens/Day (24h) | ~1,036,800 |
| Tokens/Month | ~31,104,000 |
| Effective Cost per 1M Tokens | £2.8614 |
GPTQ: Quality-Optimised Quantisation
GPTQ preserves model quality slightly better than INT4 for certain tasks. Here is the cost comparison against API providers:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 3090) | £2.8614 | — |
| 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. While the 3090’s 12 tok/s throughput limits monthly volume to ~31M tokens in single-stream mode, batched and queued workloads can accumulate enough volume to make the math work.
Hardware & Configuration Notes
GPTQ quantisation compresses LLaMA 3 70B to ~20 GB, leaving 4 GB free on the 3090. KV cache space is limited, so this setup works best for single-user or low-concurrency applications.
- VRAM usage: LLaMA 3 70B (GPTQ) requires approximately 20 GB VRAM. The RTX 3090 provides 24 GB, leaving 4 GB headroom for KV cache and batching.
- Quantisation: GPTQ quantisation reduces VRAM from 40 GB to ~20 GB. Fits on a single 24 GB GPU. GPTQ preserves quality slightly better than INT4 for some tasks.
- 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 (GPTQ) on RTX 3090
- Quality-critical analysis where GPTQ’s preservation advantages matter
- Single-user research and evaluation workloads
- Batch document processing where throughput is secondary to output quality
- Fine-grained content generation requiring nuanced language
- Internal tools where a handful of users need frontier-class responses
GPTQ-Quantised 70B for £89/Month
Run LLaMA 3 70B GPTQ on a dedicated RTX 3090. Quality-optimised compression, flat pricing.
