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How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B needs 67.4 GB for its weights at 4-bit, 68.4 GB with an 8K context and 68.9 GB with 32K.

Parameters
119B
Active per token
6.5B
Longest context
256K
Released
Mar 2026
License
Apache 2.0
Strongest at
Chat, Docs

Memory at each precision

Memory Mistral Small 4 119B needs at each precision and context length
PrecisionWeightsWith 8KWith 32KWith 128K
As released (FP8)113 GB114 GB114 GB116 GB
6-bit91.2 GB92.2 GB92.7 GB94.8 GB
5-bit79.1 GB80.1 GB80.6 GB82.7 GB
4-bit67.4 GB68.4 GB68.9 GB71.1 GB
3-bit54.3 GB55.3 GB55.9 GB58.0 GB
2-bit41.7 GB42.7 GB43.2 GB45.3 GB
1.6-bit22.9 GB23.9 GB24.5 GB26.6 GB

One conversation, 16-bit context cache, runtime overhead included.

What runs it

The best way Mistral Small 4 119B runs on common GPUs, Macs and AI boxes
MachineBest fitContextSpeedFirst reply
RTX 3060 12 GBToo big
RTX 4060 Ti 16 GBToo big
RTX 3090Too big
RTX 4090Too big
RTX 5090Too big
2× RTX 30902-bit104K170 tok/s0.7 s
RTX PRO 6000 Blackwell5-bit256K171 tok/s0.3 s
M4 Max 128 GB5-bit256K55 tok/s6.5 s
M5 Max 128 GB5-bit256K60 tok/s1.9 s
Ryzen AI Max+ 395 128 GB5-bit256K30 tok/s3.8 s
DGX Spark 128 GBFP848K27 tok/s2.5 s
M3 Ultra 256 GBFP8256K57 tok/s3.7 s
M5 Ultra 512 GBFP8256K74 tok/s1.1 s
H100 SXM4-bit256K260 tok/s0.3 s
DGX Station GB300FP8256K287 tok/s0.1 s
8× H200 NVLFP8256K243 tok/s0.1 s

Best fit is the highest quality, up to 8-bit, that leaves 16K of context. First reply is the wait for a 2K prompt. Speeds are rough, plus or minus 25%.

How to run it

llama.cpp
llama-server -hf unsloth/Mistral-Small-4-119B-2603-GGUF
vLLM
vllm serve mistralai/Mistral-Small-4-119B-2603
MLX (Mac)
mlx_lm.chat --model mlx-community/Mistral-Small-4-119B-2603-4bit

Questions

How much VRAM does Mistral Small 4 119B need?

Mistral Small 4 119B needs 67.4 GB for its weights at 4-bit, 68.4 GB with an 8K context and 68.9 GB with 32K.

Can Mistral Small 4 119B run on an RTX 4090?

Not on one card. The smallest machine here that runs it is the 2× RTX 3090.

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