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 ~29 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 | 29.0 tok/s | 3643 ms | 115K |
| Coding | S | Runs well | 29.0 tok/s | 6679 ms | 115K |
| Agentic Coding | S | Runs well | 29.0 tok/s | 9715 ms | 115K |
| Reasoning | S | Runs well | 29.0 tok/s | 7894 ms | 115K |
| RAG | S | Runs well | 29.0 tok/s | 12144 ms | 115K |
Inference speed
Estimated decode speed (tokens/sec) for Pixtral Large 124B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~8 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 8.0 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 8.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 6.2 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 5.9 | Offloads |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.9 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 3.6 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.4 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.2 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 2.0 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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 | 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 |
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: 29.0 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 29.0 tokens per second decode speed with a time-to-first-token of 6679ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on Intel Data Center GPU Max 1550 128GB receives a S grade with 29.0 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.
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.
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: