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Mistral Hosting with Ollama offers a fast, containerized way to run open-weight Mistral models locally or on servers with minimal setup. Ollama supports models like mistral, mistral-instruct, mistral-openorca, and mistral-nemo through a simple CLI and HTTP API interface, making it ideal for developers and lightweight production use.
|
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 |
Mistral Hosting with vLLM + Hugging Face provides a powerful, scalable solution for deploying Mistral models in production environments. Combining the speed and efficiency of the vLLM inference engine with the flexibility of Hugging Face Transformers, this setup supports high-throughput, low-latency serving of base and instruction-tuned Mistral models such as mistral-7B, mistral-instruct, mistral-openorca, and mistral-nemo.
|
Model Name
|
Size (16-bit Quantization
|
Recommended GPUs
|
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-2506mistralai/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 |
Hosting Qwen models — such as Qwen-1.5B, Qwen-7B, Qwen-14B, or Qwen-72B — requires a carefully designed hardware + software stack to ensure fast, scalable, and cost-efficient inference. These models are powerful but resource-intensive, and standard infrastructure often fails to meet their performance and memory requirements.
Mistral models—especially larger ones like Mixtral-8x7B—require substantial GPU memory (24GB–80GB) for inference. Without specialized GPUs (e.g., A100, L40S, 4090), full-precision or multi-user workloads become inefficient or impossible to run.
To achieve low latency and high throughput, especially in real-time applications, Mistral hosting benefits from optimized inference engines like vLLM, which support advanced techniques such as continuous batching and paged attention.
Mistral models are available in multiple formats (FP16, INT8, GGUF, AWQ), requiring compatible runtimes like Ollama, llama.cpp, or vLLM. Hosting stacks must support these toolchains to balance speed, memory, and accuracy.
Running Mistral in production often involves serving multiple concurrent requests, managing memory efficiently, and integrating with OpenAI-compatible APIs. A specialized software stack enables proper model loading, queue handling, and endpoint management for scalable deployments.
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