Mistral Hosting Service provides optimized deployment environments for the entire Mistral model family, including mistral-small, mistral-nemo, and community fine-tuned models like mistral-openorca. Whether you're serving chatbots, agents, or instruction-following applications, our platform supports both vLLM for high-throughput, production-grade APIs and Ollama for local, containerized development. Enjoy flexible GPU configurations, quantized model support (INT4/AWQ), and OpenAI-compatible endpoints for seamless integration.
| Model Name | Size (4-bit Quantization) | Recommended GPUs | Tokens/s |
|---|---|---|---|
| mistral:7b, mistral-openorca:7b, mistrallite:7b, dolphin-mistral:7b | 4.1-4.4GB | T1000 < RTX3060 < RTX4060 < RTX5060 | 23.79-73.17 |
| mistral-nemo:12b | 7.1GB | A4000 < V100 | 38.46-67.51 |
| mistral-small:22b, mistral-small:24b | 13-14GB | A5000 < RTX4090 < RTX5090 | 37.07-65.07 |
| mistral-large:123b | 73GB | A100-80gb < H100 | ~30 |
| Model Name | Size (16-bit Quantization) | Recommended GPU(s) | Concurrent Requests | Tokens/s |
|---|---|---|---|---|
| mistralai/Pixtral-12B-2409 | ~25GB | A100-40gb < A6000 < 2*RTX4090 | 50 | 713.45-861.14 |
| mistralai/Mistral-Small-3.2-24B-Instruct-2506 mistralai/Mistral-Small-3.1-24B-Instruct-2503 | ~47GB | 2*A100-40gb < H100 | 50 | ~1200-2000 |
| mistralai/Pixtral-Large-Instruct-2411 | 292GB | 8*A6000 | 50 | ~466.32 |
Express GPU VPS - 2GB
Lite Dedicated GPU Server - P600
Express Dedicated GPU Server - P1000
Basic Dedicated GPU Server - K80
Basic GPU VPS - RTX 5060
Basic Dedicated GPU Server - T1000
Basic Dedicated GPU Server - GTX 1650
Professional GPU VPS - RTX Pro 2000
Basic Dedicated GPU Server - GTX 1660
Professional GPU VPS - RTX A4000
Basic Dedicated GPU Server - RTX 4060
Basic Dedicated GPU Server - RTX 5060
Professional Dedicated GPU Server - P100
Professional Dedicated GPU Server - RTX 2060
Advanced GPU VPS - RTX Pro 4000
Advanced Dedicated GPU Server - RTX 2060
Advanced Dedicated GPU Server - RTX 3060 Ti
Advanced Dedicated GPU Server - RTX A4000
Advanced Dedicated GPU Server - V100
Advanced Dedicated GPU Server - RTX A5000
Advanced GPU VPS - RTX Pro 5000
Advanced GPU VPS - RTX 5090
Enterprise Dedicated GPU Server - RTX 4090
Enterprise Dedicated GPU Server - RTX A6000
Enterprise Dedicated GPU Server - A40
Enterprise Dedicated GPU Server - RTX 5090
Enterprise GPU VPS - RTX Pro 6000
Enterprise Multi-GPU Dedicated Server - 3xV100
Enterprise Multi-GPU Dedicated Server - 3xRTX A5000
Enterprise Dedicated GPU Server - A100
Enterprise Multi-GPU Dedicated Server - 2xRTX 4090
Enterprise Multi-GPU Dedicated Server - 2xRTX 5090
Enterprise Multi-GPU Dedicated Server - 3xRTX A6000
Enterprise Multi-GPU Dedicated Server - 4xRTX A6000
Enterprise Dedicated GPU Server - A100(80GB)
Enterprise Multi-GPU Dedicated Server - 4xA100
Enterprise Dedicated GPU Server - H100
Mistral Hosting is to deploying open source Mistral large language models (such as Mistral-7B, Mixtral-8x7B, Pixtral-12B, etc.) on dedicated hardware for local or remote reasoning. Users can choose self-hosted deployment, that is, running the model on a local or cloud GPU server, combined with reasoning frameworks such as vLLM, Ollama, llama.cpp, etc., with full control over data, performance, and model configuration, suitable for enterprises or technical teams with high requirements for privacy, security, and customization.
Another way is to use Mistral as a Service (Mistral as a Service), which can call the model through the API provided by official or third-party platforms (such as mistral.ai, Together.ai, Fireworks.ai), without infrastructure configuration, and is more suitable for prototype development, lightweight applications, and rapid integration. However, compared with self-hosted deployment, this method will sacrifice cost control, model customization, and data security. Which method you choose depends on your usage scenario, technical capabilities, and need for control.
✅ High-memory GPUs: NVIDIA A100 (40GB/80GB), L40S, H100, or RTX 4090 with at least 24GB VRAM
✅ High-bandwidth NVLink or PCIe: For multi-GPU setups to support tensor parallelism
✅CPU & RAM: Multi-core CPUs (16+ threads), 64–128GB RAM recommended for concurrent inference
✅RAM: 64GB–512GB system memory (depends on parallelism & model size)
✅ Storage: Fast NVMe SSDs for model loading and disk-based KV cache if supported
✅ Model Format: Hugging Face Transformers, GGUF (for llama.cpp/Ollama), or AWQ/GPTQ quantized weights
✅ Inference Engine: vLLM, Ollama, llama.cpp
✅ Serving Tools: FastAPI, OpenAI-compatible APIs, TGI (Text Generation Inference), Docker
✅ Optional Add-ons: LoRA fine-tuning loaders, quantization tools (AutoAWQ, GPTQ), monitoring stack (Prometheus, Grafana)
| Feature | Self-hosted Mistral Hosting | Mistral as a Service |
|---|---|---|
| Control & Customization | Full control over model, hardware, tuning, and privacy | Limited control; model behavior is managed by vendor |
| Deployment Location | On-premise or private cloud (user-managed) | Public cloud (vendor-managed) |
| Initial Setup Effort | High (requires DevOps, infra setup, model configuration) | Low (ready-to-use APIs) |
| Scalability | Manual scaling; needs infrastructure planning | Auto-scaled by provider |
| Cost Structure | High upfront cost, low long-term cost for heavy usage | Pay-as-you-go; better for low/medium usage |
| Supported Models | Any version or quantized variant (FP16, INT8, AWQ, etc.) | Limited to provider's available models |
| Latency | Low (local or same-region inference) | Depends on provider's API and region |
| Data Privacy | High (data stays within controlled environment) | Lower (data sent to external APIs) |
| Best For | Enterprises, privacy-focused apps, custom workloads | Startups, rapid prototyping, non-critical use cases |
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