How much VRAM does Ministral 3 14B need?

27.9 GB for its weights at 16-bit, 13.9 GB at 8-bit and 7.84 GB at 4-bit, plus the KV cache: 164 KB for each token, 5.37 GB for a conversation of 32K tokens and 42.9 GB at its longest context of 262,144 tokens. The server also keeps about 8% of each GPU free.

Can Ministral 3 14B run on 1 GPU?

Yes, at 4-bit: the smallest single GPU here that holds it with a 32K conversation is the L4.

How many H100s does Ministral 3 14B need?

With 1 conversation of 32K tokens, 1 H100 at 16-bit and 1 H100 at 4-bit. Each H100 has 80 GB, of which vLLM uses 92% by default.

Can I run Ministral 3 14B on an RTX 5090?

The RTX 5090 has 32 GB. With a 32K conversation, Ministral 3 14B needs 1 GeForce RTX 5090 at 4-bit and 2 GeForce RTX 5090s at 16-bit.

How fast does Ministral 3 14B run?

At 4-bit on 1 H100, memory lets 1 conversation get at most 254 tokens a second. Real servers reach part of that ceiling, and each conversation gets less when many run at once.

How much GPU memory does Ministral 3 14B need?

Ministral 3 14B by Mistral AI has 13.9B parameters. Its weights take 27.9 GB at 16-bit, 13.9 GB at 8-bit and 7.84 GB at 4-bit; released in FP8, its files are 15.7 GB. Each token of a conversation adds 164 KB of KV cache, so a 32K conversation holds 5.37 GB.

Size Ministral 3 14B for your setup Questions

Mistral AI · released in FP8

Ministral 3 14B at a glance

Parameters: 13.9B
As released: 15.7 GB; Its FP8 files
At 16-bit: 27.9 GB
At 4-bit: 7.84 GB; NVFP4
KV cache a token: 164 KB; At 16-bit
Longest context: 262,144; Tokens

GPUs needed at each number format

For 1 conversation of 32,768 tokens with a 16-bit KV cache, counting 92% of each GPU's memory as vLLM does. Under each count, the most tokens a second memory allows.

GPU	Memory	16-bit	8-bit	4-bit
L4	24 GB	2 GPUs18 tokens/s	1 GPU16 tokens/s	1 GPU23 tokens/s
GeForce RTX 5090	32 GB	2 GPUs108 tokens/s	1 GPU93 tokens/s	1 GPU136 tokens/s
L40S	48 GB	1 GPU26 tokens/s	1 GPU45 tokens/s	1 GPU65 tokens/s
A100 80GB	80 GB	1 GPU61 tokens/s	1 GPU106 tokens/s	1 GPU154 tokens/s
H100	80 GB	1 GPU101 tokens/s	1 GPU173 tokens/s	1 GPU254 tokens/s
RTX PRO 6000 Blackwell	96 GB	1 GPU54 tokens/s	1 GPU93 tokens/s	1 GPU136 tokens/s
DGX Spark	128 GB	1 GPU8.2 tokens/s	1 GPU14 tokens/s	1 GPU21 tokens/s
H200	141 GB	1 GPU144 tokens/s	1 GPU249 tokens/s	1 GPU363 tokens/s
B200	180 GB	1 GPU241 tokens/s	1 GPU414 tokens/s	1 GPU605 tokens/s
Instinct MI300X	192 GB	1 GPU159 tokens/s	1 GPU274 tokens/s	1 GPU401 tokens/s
Instinct MI325X	256 GB	1 GPU180 tokens/s	1 GPU311 tokens/s	1 GPU454 tokens/s
Instinct MI355X	288 GB	1 GPU241 tokens/s	1 GPU414 tokens/s	1 GPU605 tokens/s

Models of a similar size

Every model

Questions

How much VRAM does Ministral 3 14B need?: 27.9 GB for its weights at 16-bit, 13.9 GB at 8-bit and 7.84 GB at 4-bit, plus the KV cache: 164 KB for each token, 5.37 GB for a conversation of 32K tokens and 42.9 GB at its longest context of 262,144 tokens. The server also keeps about 8% of each GPU free.
Can Ministral 3 14B run on 1 GPU?: Yes, at 4-bit: the smallest single GPU here that holds it with a 32K conversation is the L4.
How many H100s does Ministral 3 14B need?: With 1 conversation of 32K tokens, 1 H100 at 16-bit and 1 H100 at 4-bit. Each H100 has 80 GB, of which vLLM uses 92% by default.
Can I run Ministral 3 14B on an RTX 5090?: The RTX 5090 has 32 GB. With a 32K conversation, Ministral 3 14B needs 1 GeForce RTX 5090 at 4-bit and 2 GeForce RTX 5090s at 16-bit.
How fast does Ministral 3 14B run?: At 4-bit on 1 H100, memory lets 1 conversation get at most 254 tokens a second. Real servers reach part of that ceiling, and each conversation gets less when many run at once.

Sources

Ministral 3 14B at a glance

GPUs needed at each number format

Models of a similar size

Questions

Where each fact comes from