Mistral Large Instruct 2407: Self-Hosting & Deployment Guide
Mistral Large Instruct 2407 is Mistral's 123B open-weight flagship for research and self-hosting.
What is Mistral Large Instruct 2407?
Mistral Large Instruct 2407 is Mistral's 123B open-weight flagship for research and self-hosting, published by Mistral AI with open weights at mistralai/Mistral-Large-Instruct-2407. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly.
How to run Mistral Large Instruct 2407 yourself
Open weights (other), 122.6B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
Mistral Large Instruct 2407 VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 245.2 GB | Estimate | 122.6B params × 2 bytes |
| FP8 | 123.4 GB | File size | RedHatAI/Mistral-Large-Instruct-2407-FP8 |
| AWQ 4-bit | 64.9 GB | File size | casperhansen/mistral-large-instruct-2407-awq |
| MLX 4-bit | 69 GB | File size | mlx-community/Mistral-Large-Instruct-2407-4bit |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy Mistral Large Instruct 2407 on your hardware
| Hardware | BF16 (full precision) | FP8 | AWQ 4-bit | MLX 4-bit |
|---|---|---|---|---|
| NVIDIA H200141 GB | 2× | 2× | 1× | 1× |
| NVIDIA H10080 GB | 4× | 2× | 1× | 1× |
| NVIDIA A100 80GB80 GB | 4× | 2× | 1× | 1× |
| NVIDIA L40S48 GB | 6× | 3× | 2× | 2× |
GPUs needed per variant.
| Hardware | MLX 4-bit |
|---|---|
| Mac, 512 GBM3 Ultra | Fits |
| Mac, 192 GBM2 Ultra | Fits |
| Mac, 128 GBM4 Max / M3 Max | Fits |
| Mac, 64 GBM4 Pro / Max | — |
Assumes ~75% of unified memory is usable by the GPU.
| Hardware | BF16 (full precision) | FP8 | AWQ 4-bit | MLX 4-bit |
|---|---|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits | Fits | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits | Fits | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | — | Fits | Fits | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits | Fits | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits | Fits | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
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