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All estimates are approximations based on mathematical models and public specifications. Actual performance may vary. Do not make purchasing decisions based solely on these estimates.

Data sourced from Hugging Face, Ollama, and official model documentation. Model names and logos are trademarks of their respective owners.

© 2026 Will It Run AI — Fase Consulting Ibiza, S.L. (NIF: B57969656)

Home/Devstral 2 123B Instruct/on RTX 4090 Laptop 16GB

Can it run?

Can RTX 4090 Laptop 16GB run Devstral 2 123B Instruct?

FWon't run

Too heavy

Using Q4_K_M in vLLM

Capabilities:

Fit status

Too heavy

Decode

6.1 tok/s

TTFT

31523 ms

Safe context

4K

Memory

98.2 GB / 16.0 GB

Memory breakdown

Weights75.0 GB
KV Cache19.2 GB
Runtime2.4 GB
Headroom1.6 GB

Performance by workload

WorkloadGradeFitDecodeTTFTContext
Agentic CodingFToo heavy6.1 tok/s45852 ms4K
ChatFToo heavy6.1 tok/s17194 ms4K
CodingFToo heavy6.1 tok/s31523 ms4K
RAGFToo heavy6.1 tok/s57315 ms4K
ReasoningFToo heavy6.1 tok/s37254 ms4K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
48.0 GB
LowF0
Q3_K_S
3
60.3 GB
LowF0
NVFP4
4
68.9 GB
MediumF0
Q4_K_M
4
75.0 GB
MediumF0
Q5_K_M
5
88.6 GB
HighF0
Q6_K
6
100.9 GB
HighF0
Q8_0
8
131.6 GB
Very HighF0
F16
16
252.2 GB
MaximumF0

Upgrade options

Hardware that runs Devstral 2 123B Instruct well

AppleMacBook Pro M3 Max 128GBBest value
D3 tok/s decode

~$2,499 MSRP

AMDAMD Instinct MI350X 288GBBudget pick
C77.8 tok/s decode

~$8,000 MSRP

NVIDIANVIDIA B200 180GBBiggest leap
B89.6 tok/s decode

~$30,000 MSRP

NVIDIANVIDIA H200 141GBNVIDIA upgrade
B53.7 tok/s decode

~$30,000 MSRP

See all results for RTX 4090 Laptop 16GBSee all hardware for Devstral 2 123B Instruct