Mixtral 8x7B on RTX 5090: Monthly Cost & Token Output
Dedicated RTX 5090 hosting for Mixtral 8x7B (46.7B) inference — fixed monthly pricing with unlimited tokens.
Monthly Cost Summary
Mixtral 8x7B’s mixture-of-experts architecture delivers GPT-3.5-class quality in an open-source package. The RTX 5090 is the only single consumer GPU with enough VRAM to run the full 46.7B parameter model in FP16. At £179/month, you get 190 million tokens of monthly capacity with zero vendor lock-in.
| Metric | Value |
|---|---|
| GPU | RTX 5090 (32 GB VRAM) |
| Model | Mixtral 8x7B (46.7B parameters) |
| Monthly Server Cost | £179/mo |
| Tokens/Second | ~73.5 tok/s |
| Tokens/Day (24h) | ~6,350,400 |
| Tokens/Month | ~190,512,000 |
| Effective Cost per 1M Tokens | £0.9396 |
Open-Source Quality, Dedicated Hardware Pricing
Mixtral 8x7B through API providers can get expensive at scale. Here is where self-hosted hardware stands:
| Provider | Cost per 1M Tokens | GigaGPU Savings |
|---|---|---|
| GigaGPU (RTX 5090) | £0.9396 | — |
| Together.ai | $0.60 | Comparable |
| Fireworks | $0.50 | Comparable |
| Groq | $0.24 | Comparable |
Break-Even Analysis
Compared to Groq at $0.24/1M tokens, break-even arrives at approximately 745.8M tokens/month. Mixtral’s MoE architecture is efficient at inference time — only ~13B parameters activate per forward pass — making the 73.5 tok/s throughput strong for a model of this quality class.
Hardware & Configuration Notes
Mixtral 8x7B requires ~26 GB VRAM, leaving 6 GB free on the 5090’s 32 GB. That is tight but workable for moderate batching. INT4 quantisation can free significant additional VRAM.
- VRAM usage: Mixtral 8x7B requires approximately 26 GB VRAM. The RTX 5090 provides 32 GB, leaving 6 GB headroom for KV cache and batching.
- Quantisation: Running in FP16 by default. INT8 or INT4 quantisation can reduce VRAM usage and increase throughput by 20–40% with minimal quality loss for most use cases.
- 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 Mixtral 8x7B on RTX 5090
- Production chatbots requiring GPT-3.5-class quality without API dependency
- Complex reasoning and multi-step task completion
- Enterprise document analysis and question answering
- Code generation with strong instruction-following
- High-quality content generation at scale
GPT-3.5-Class Quality, £399/Month
Run Mixtral 8x7B on a dedicated RTX 5090. No per-token charges, no vendor lock-in, full model control.
