AI Model
Mixtral 8x7B
47B parameters · text-generation
VRAM (FP16)
100 GB
VRAM (INT4)
28 GB
Family
mixtral
Compatible GPUs
AMD Instinct MI250X
Min GPUs: 1 · fp16
NVIDIA H200
Min GPUs: 1 · fp16
NVIDIA H200 NVL
Min GPUs: 1 · fp16
NVIDIA B200
Min GPUs: 1 · fp16
NVIDIA GB200
Min GPUs: 1 · fp16
AMD Instinct MI300X
Min GPUs: 1 · fp16
NVIDIA B300
Min GPUs: 1 · fp16
NVIDIA A16
Min GPUs: 2 · fp16
NVIDIA H100
Min GPUs: 2 · fp16
NVIDIA A100
Min GPUs: 2 · fp16
NVIDIA GH200
Min GPUs: 2 · fp16
NVIDIA L40S
Min GPUs: 3 · fp16
NVIDIA A40
Min GPUs: 3 · fp16
NVIDIA RTX 6000 Ada
Min GPUs: 3 · fp16
NVIDIA RTX A6000
Min GPUs: 3 · fp16
NVIDIA V100
Min GPUs: 4 · fp16
NVIDIA RTX 5090
Min GPUs: 4 · fp16
NVIDIA L4
Min GPUs: 5 · fp16
NVIDIA A10
Min GPUs: 5 · fp16
NVIDIA A10G
Min GPUs: 5 · fp16
NVIDIA RTX 4090
Min GPUs: 5 · fp16
NVIDIA RTX A5000
Min GPUs: 5 · fp16
NVIDIA RTX 3090
Min GPUs: 5 · fp16
NVIDIA T4
Min GPUs: 7 · fp16
NVIDIA P100
Min GPUs: 7 · fp16
NVIDIA RTX 5080
Min GPUs: 7 · fp16
NVIDIA RTX A4000
Min GPUs: 7 · fp16
Supported Frameworks
vLLMSGLangPyTorchTensorRT-LLMText Generation Inference
Deploy Mixtral 8x7B
Get a full deployment stack recommendation — GPU, count, framework, quantization, and projected cost.
Start deploymentVRAM Usage
FP16 serving needs about 100 GB before workload-specific headroom. INT4 quantization reduces the model weights to about 28 GB, which is the practical path for large models on smaller GPU clusters.
Related Mixtral 8x7B resources
Move from model requirements into compatible GPU prices, deployment, and the wider model catalog.
AMD Instinct MI250X prices for Mixtral 8x7BRecommended GPU path for this model at fp16 precision.NVIDIA H200 cloud pricesCompatible option for Mixtral 8x7B; minimum 1 GPU.NVIDIA H200 NVL cloud pricesCompatible option for Mixtral 8x7B; minimum 1 GPU.NVIDIA B200 cloud pricesCompatible option for Mixtral 8x7B; minimum 1 GPU.Deploy Mixtral 8x7BGenerate a deployment recommendation with GPU count, framework, and estimated cost.Model VRAM leaderboardCompare FP16 and INT4 memory requirements across other deployable models.