Pixtral Large 124B needs ~94.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~27 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
29.0 tok/s
TTFT
6679 ms
Safe context
115K
Memory
94.7 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 | S | Runs well | 26.7 tok/s | 3962 ms | 115K |
| Coding | S | Runs well | 26.7 tok/s | 7264 ms | 115K |
| Agentic Coding | S | Runs well | 26.7 tok/s | 10566 ms | 115K |
| Reasoning | S | Runs well | 26.7 tok/s | 8585 ms | 115K |
| RAG | S | Runs well | 26.7 tok/s | 13207 ms | 115K |
How Pixtral Large 124B (124B 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 | 48.4 GB | Low | A84 |
Q3_K_S | 3 | 60.8 GB | Low | S86 |
NVFP4 | 4 |
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server startYes, Intel Data Center GPU Max 1550 128GB can run Pixtral Large 124B with a S grade (Runs well). Expected decode speed: 26.7 tok/s.
Pixtral Large 124B (124B parameters) requires approximately 94.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Pixtral Large 124B is Q4_K_M, which balances quality and memory efficiency.
On Intel Data Center GPU Max 1550 128GB, Pixtral Large 124B achieves approximately 26.7 tokens per second decode speed with a time-to-first-token of 7264ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on Intel Data Center GPU Max 1550 128GB receives a S grade with 26.7 tok/s and 115K context.
On Intel Data Center GPU Max 1550 128GB, Pixtral Large 124B can safely use up to 115K tokens of context. The model's official context limit is 131K, 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/pixtral-large-124b-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:
69.4 GB |
| Medium |
| S87 |
Q4_K_M | 4 | 75.6 GB | Medium | S87 |
Q5_K_M | 5 | 89.3 GB | High | S87 |
Q6_KBest for your GPU | 6 | 101.7 GB | High | S87 |
Q8_0 | 8 | 132.7 GB | Very High | F0 |
F16 | 16 | 254.2 GB | Maximum | F0 |
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.