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August 23, 2026No. 113
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Mistral AI · Codestral

Will Codestral 22B v0.1 run on my GPU?

22.2B parameters, 32,768-token context, Mistral AI Non-Production License. At Q4_K_M the weights come to about 12.4 GiB; add 1.5 GiB for the runtime and you need roughly 13.9 GiB of VRAM before context.

Weights by quant

GiB
Q4_K_M · the everyday quant12.4 GiB
Q8_0 · near-lossless21.9 GiB
FP16 · full weights41.4 GiB
Runtime reserve1.5 GiB

Card by card

11 of 17 fit at Q4
CardVRAMQ4_K_MQ8_0FP16Headroom at Q4
GeForce RTX 5090NVIDIA 32GB YesYesOffload 18.1 GiB
GeForce RTX 3090 (used)NVIDIA · used market 24GB YesTightOffload 10.1 GiB
GeForce RTX 4090 (used)NVIDIA · used market 24GB YesTightOffload 10.1 GiB
Radeon RX 7900 XTXAMD 24GB YesTightOffload 10.1 GiB
GeForce RTX 5060 Ti 16GBNVIDIA 16GB TightOffloadNo 2.1 GiB
GeForce RTX 5070 TiNVIDIA 16GB TightOffloadNo 2.1 GiB
GeForce RTX 5080NVIDIA 16GB TightOffloadNo 2.1 GiB
Radeon RX 7800 XTAMD 16GB TightOffloadNo 2.1 GiB
Radeon RX 9060 XT 16GBAMD 16GB TightOffloadNo 2.1 GiB
Radeon RX 9070AMD 16GB TightOffloadNo 2.1 GiB
Radeon RX 9070 XTAMD 16GB TightOffloadNo 2.1 GiB
Arc B580Intel 12GB OffloadOffloadNo short by 1.9 GiB
GeForce RTX 3060 12GB (used)NVIDIA · used market 12GB OffloadOffloadNo short by 1.9 GiB
GeForce RTX 4070 SuperNVIDIA · used market 12GB OffloadOffloadNo short by 1.9 GiB
GeForce RTX 5070NVIDIA 12GB OffloadOffloadNo short by 1.9 GiB
Arc B570Intel 10GB OffloadNoNo short by 3.9 GiB
GeForce RTX 5060NVIDIA 8GB OffloadNoNo short by 5.9 GiB

Headroom is what remains for the KV cache after weights and the 1.5 GiB reserve. How far it stretches depends on the architecture: grouped-query models are frugal, older dense models are not. If the table says tight, plan on a shorter context or a lower quant. Parameter count from the model's own card.

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