Codestral 22B v0.1 needs ~29.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~150 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
Runs well
Decode
150.2 tok/s
TTFT
1289 ms
Safe context
626K
Memory
29.7 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 150.2 tok/s | 703 ms | 626K |
| Coding | C | Runs well | 150.2 tok/s | 1289 ms | 626K |
| Agentic Coding | C | Runs well | 150.2 tok/s | 1875 ms | 626K |
| Reasoning | C | Runs well | 150.2 tok/s | 1523 ms | 626K |
| RAG | C | Runs well | 150.2 tok/s | 2343 ms | 626K |
How Codestral 22B v0.1 (22B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | D38 |
Q3_K_S | 3 | 10.8 GB | Low | D38 |
NVFP4 | 4 |
Copy-paste commands to run Codestral 22B v0.1 on your machine.
Run
lms load hf-lmstudio-community--codestral-22b-v0-1-gguf && lms server startYes, Intel Data Center GPU Max 1550 128GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 150.2 tok/s.
Codestral 22B v0.1 (22B parameters) requires approximately 29.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Codestral 22B v0.1 achieves approximately 150.2 tokens per second decode speed with a time-to-first-token of 1289ms using Q4_K_M quantization.
For coding workloads, Codestral 22B v0.1 on Intel Data Center GPU Max 1550 128GB receives a C grade with 150.2 tok/s and 626K context.
On Intel Data Center GPU Max 1550 128GB, Codestral 22B v0.1 can safely use up to 626K tokens of context. The model's official context limit is —, 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/hf-lmstudio-community--codestral-22b-v0-1-gguf-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
12.3 GB |
| Medium |
| D38 |
Q4_K_M | 4 | 13.4 GB | Medium | D38 |
Q5_K_M | 5 | 15.8 GB | High | D39 |
Q6_K | 6 | 18.0 GB | High | D39 |
Q8_0 | 8 | 23.5 GB | Very High | D39 |
F16Best for your GPU | 16 | 45.1 GB | Maximum | C43 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.