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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/Codestral 2 25.08/on RTX 4070 Ti Super 16GB

Can it run?

Can RTX 4070 Ti Super 16GB run Codestral 2 25.08?

FWon't run

Too heavy

Using Q4_K_M in vLLM

Capabilities:

Fit status

Too heavy

Decode

40.1 tok/s

TTFT

4833 ms

Safe context

12K

Memory

20.9 GB / 16.0 GB

Memory breakdown

Weights13.4 GB
KV Cache3.4 GB
Runtime2.4 GB
Headroom1.6 GB

Performance by workload

WorkloadGradeFitDecodeTTFTContext
Agentic CodingFToo heavy40.1 tok/s7030 ms21K
ChatDVery compromised (needs ~2.2 GB host RAM)34.5 tok/s3063 ms7K
CodingFToo heavy40.1 tok/s4833 ms12K
RAGFToo heavy40.1 tok/s8787 ms21K
ReasoningFToo heavy40.1 tok/s5711 ms12K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.6 GB
LowC41
Q3_K_SBest for your GPU
3
10.8 GB
LowC44
NVFP4
4
12.3 GB
MediumC44
Q4_K_M
4
13.4 GB
MediumC44
Q5_K_M
5
15.8 GB
HighC44
Q6_K
6
18.0 GB
HighF0
Q8_0
8
23.5 GB
Very HighF0
F16
16
45.1 GB
MaximumF0

Upgrade options

Hardware that runs Codestral 2 25.08 well

AppleMacBook Pro M4 32GBBest value
C6.4 tok/s decode

~$799 MSRP

AMDRX 7900 XTX 24GBBudget pick
C51.5 tok/s decode

~$999 MSRP

NVIDIARTX 5090 32GBBiggest leap
B89.5 tok/s decode

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBNVIDIA upgrade
B56.1 tok/s decode

 

See all results for RTX 4070 Ti Super 16GBSee all hardware for Codestral 2 25.08