Codestral 2 25.08 needs ~18.8 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
20.1 tok/s
TTFT
9638 ms
Safe context
24K
Memory
18.8 GB / 20.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 20.1 tok/s | 5257 ms | 24K |
| Coding | A | Tight fit | 20.1 tok/s | 9638 ms | 24K |
| Agentic Coding | A | Runs with offload (needs ~0.8 GB host RAM) | 13.3 tok/s | 21138 ms | 24K |
| Reasoning | A | Tight fit | 20.1 tok/s | 11391 ms | 24K |
| RAG | A | Runs with offload (needs ~0.8 GB host RAM) | 13.3 tok/s | 26422 ms | 24K |
How Codestral 2 25.08 (22B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | A84 |
Q3_K_S | 3 | 10.8 GB | Low | A85 |
NVFP4 | 4 | 12.3 GB | Medium | A85 |
Q4_K_MBest for your GPU | 4 | 13.4 GB | Medium | A84 |
Q5_K_M | 5 | 15.8 GB | High | F0 |
Q6_K | 6 | 18.0 GB | High | F0 |
Q8_0 | 8 | 23.5 GB | Very High | F0 |
F16 | 16 | 45.1 GB | Maximum | F0 |
Copy-paste commands to run Codestral 2 25.08 on your machine.
Run
lms load codestral-2508 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 23.8 tok/s | |
| Qwen 3.5 27B | 27B | A | 10.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 10.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 25.3 tok/s | |
 Magistral Small 2507 | 24B | S | 20.6 tok/s |
Yes, RTX 4000 Ada 20GB can run Codestral 2 25.08 with a A grade (Tight fit). Expected decode speed: 20.1 tok/s.
Codestral 2 25.08 (22B parameters) requires approximately 18.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, Codestral 2 25.08 achieves approximately 20.1 tokens per second decode speed with a time-to-first-token of 9638ms using Q4_K_M quantization.
For coding workloads, Codestral 2 25.08 on RTX 4000 Ada 20GB receives a A grade with 20.1 tok/s and 24K context.
On RTX 4000 Ada 20GB, Codestral 2 25.08 can safely use up to 24K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/codestral-2-25.08-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: