Codestral 2 25.08 needs ~18.4 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 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
2.4 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.7 GB host RAM)
Decode
25.9 tok/s
TTFT
7474 ms
Safe context
4K
Memory
18.4 GB / 16.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.9 GB host RAM) | 29.9 tok/s | 3527 ms | 4K |
| Coding | A | Very compromised (needs ~1.7 GB host RAM) | 25.9 tok/s | 7474 ms | 4K |
| Agentic Coding | F | Too heavy | 19.9 tok/s | 14138 ms | 4K |
| Reasoning | A | Very compromised (needs ~1.7 GB host RAM) | 25.9 tok/s | 8833 ms | 4K |
| RAG | F | Too heavy | 19.9 tok/s | 17673 ms |
How Codestral 2 25.08 (22B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | S86 |
Q3_K_SBest for your GPU | 3 | 10.8 GB | Low | S85 |
Copy-paste commands to run Codestral 2 25.08 on your machine.
Run
lms load codestral-2508 && lms server startYes, RTX 5080 Laptop 16GB can run Codestral 2 25.08 with a A grade (Very compromised (needs ~1.7 GB host RAM)). Expected decode speed: 25.9 tok/s.
Codestral 2 25.08 (22B parameters) requires approximately 18.4 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 5080 Laptop 16GB, Codestral 2 25.08 achieves approximately 25.9 tokens per second decode speed with a time-to-first-token of 7474ms using Q4_K_M quantization.
For coding workloads, Codestral 2 25.08 on RTX 5080 Laptop 16GB receives a A grade with 25.9 tok/s and 4K context.
On RTX 5080 Laptop 16GB, Codestral 2 25.08 can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/codestral-2-25.08-on-rtx-5080-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
12.3 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 13.4 GB | Medium | F0 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.