Mistral-Small-4-119B-2603 needs about 121 GB of GPU memory for FP8 weights. The smallest fitting setup at 32k context is 1× NVIDIA B200. It supports 64 concurrent conversations with SGLang defaults.
GPU requirements by context length
8k context
GPU
GPUs
Concurrent users
NVIDIA B200
1
64
NVIDIA H200 SXM
2
64
NVIDIA H100 SXM
2
64
NVIDIA H100 NVL
2
64
NVIDIA A100 80GB SXM
2
58
NVIDIA RTX PRO 6000 Blackwell Server Edition
2
64
NVIDIA H20
2
64
NVIDIA L40S
4
64
32k context
GPU
GPUs
Concurrent users
NVIDIA B200
1
64
NVIDIA H200 SXM
2
64
NVIDIA H100 SXM
2
27
NVIDIA H100 NVL
2
60
NVIDIA A100 80GB SXM
2
14
NVIDIA RTX PRO 6000 Blackwell Server Edition
2
64
NVIDIA H20
2
64
NVIDIA L40S
4
23
128k context
GPU
GPUs
Concurrent users
NVIDIA B200
1
26
NVIDIA H200 SXM
2
38
NVIDIA H100 SXM
2
6
NVIDIA H100 NVL
2
15
NVIDIA A100 80GB SXM
2
3
NVIDIA RTX PRO 6000 Blackwell Server Edition
2
16
NVIDIA H20
2
16
NVIDIA L40S
4
5
FP8 weights, SGLang defaults, estimates.
Weights by precision
Weight memory
Precision
Weights
Smallest fitting setup at 32k
FP8
about 121 GB
1x NVIDIA B200
INT4 (AWQ)
about 67 GB
1x NVIDIA B200
Model notes
Attention
Multi-head latent attention (MLA)
Architecture
Mixture-of-Experts
Layers
36
Hidden size
4,096
Vocabulary
131,072
MLA keeps one compact shared KV copy per GPU, so adding GPUs does not shrink the cache, but the copy itself is small for a model this size.
Frequently asked questions
Will mistralai/Mistral-Small-4-119B-2603 run on a single H100?
No, a single H100 SXM cannot hold mistralai/Mistral-Small-4-119B-2603 at 32k context with FP8 weights. The model requires multiple GPUs.
What is the cheapest GPU setup for mistralai/Mistral-Small-4-119B-2603?
The cheapest fitting setup at 32k context is 1x NVIDIA B200 with FP8 weights and SGLang defaults.
How much VRAM does mistralai/Mistral-Small-4-119B-2603 need at 128k context?
At 128k context on the cheapest fitting setup, mistralai/Mistral-Small-4-119B-2603 uses about 121 GB of VRAM per GPU for FP8 weights and about 1.5 GB per conversation for the KV cache. The total VRAM needed depends on the GPU count and parallelism configuration.
Can I run mistralai/Mistral-Small-4-119B-2603 with INT4 quantization?
Yes, mistralai/Mistral-Small-4-119B-2603 has INT4 weight figures. Its weights take about 67 GB in INT4 versus about 121 GB in FP8. INT4 can make the model fit on fewer GPUs, but check inference quality for your workload.
Want a different context length, GPU, or quantization? The calculator runs the same numbers live, preloaded with this model.