LLaMA 3 8B on RTX 5060 Ti: Monthly Cost & Token Output
Dedicated RTX 5060 Ti hosting for LLaMA 3 8B (8B) inference — fixed monthly pricing with unlimited tokens.
What £119/Month Actually Buys You
A single RTX 5060 Ti running LLaMA 3 8B produces approximately 71.2 tokens per second around the clock. Over a full month, that translates to roughly 184.5 million tokens — all for a flat £119 with no usage-based surcharges.
| Metric | Value |
|---|---|
| GPU | RTX 5060 Ti (16 GB VRAM) |
| Model | LLaMA 3 8B (8B parameters) |
| Monthly Server Cost | £119/mo |
| Tokens/Second | ~71.2 tok/s |
| Tokens/Day (24h) | ~6,151,680 |
| Tokens/Month | ~184,550,400 |
| Effective Cost per 1M Tokens | £0.3739 |
Cost-per-Token Compared to API Providers
The 5060 Ti’s 16 GB of VRAM gives LLaMA 3 8B comfortable headroom for KV cache and batched requests. Here is how the resulting per-token economics stack up:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5060 Ti) | £0.3739 | — |
| Together.ai | $0.18 | Comparable |
| Fireworks | $0.20 | Comparable |
| Groq | $0.05 | Comparable |
Keep in mind: API costs grow with every request. Your GigaGPU bill stays at £119 whether you process one million tokens or 184 million.
When Dedicated Hardware Pays for Itself
Comparing against Groq at $0.05 per million tokens, the break-even point is approximately 1,380M tokens/month. If your workload exceeds that, dedicated hardware wins outright on cost.
Even below break-even, the 5060 Ti offers advantages that per-token APIs cannot: data stays on your server, latency is predictable, and you have full control over model configuration and fine-tuning.
Configuration & Optimisation
- VRAM headroom: LLaMA 3 8B needs roughly 8 GB VRAM. The 5060 Ti’s 16 GB leaves 8 GB free — enough for generous KV cache allocation and multi-user batching.
- Quantisation: Running FP16 by default. INT8 or INT4 quantisation can increase throughput by 20–40% with negligible quality loss for most workloads.
- Serving framework: Deploy with vLLM or TGI for continuous batching and OpenAI-compatible API endpoints.
- Scale-out: Add more RTX 5060 Ti nodes behind a load balancer when demand grows. GigaGPU supports multi-server configurations.
Production Use Cases
- Always-on customer support chatbots
- Content generation and summarisation workflows
- Retrieval-augmented generation (RAG) for enterprise search
- Code autocompletion backends
- High-throughput batch text analysis
Lock In £119/Month — Unlimited Tokens
Spin up a dedicated RTX 5060 Ti server ready for LLaMA 3 8B. No metered billing, no rate limits, full root access.
