Can Pixtral Large 124B run on MacBook Pro M3 Max 128GB?
YES — With Offload
Pixtral Large 124B needs ~95.7 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~3 tok/s.
Operating mode
Choose the run profile you care about
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
3.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~2.8 GB host RAM)
Decode
3.2 tok/s
TTFT
60465 ms
Safe context
5K
Memory
95.7 GB / 92.2 GB
Memory breakdown
See how fast it feels
What limits this setup
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.7 GB host RAM) | 3.4 tok/s | 31364 ms | 5K |
| Coding | A | Runs with offload | 2.9 tok/s | 65755 ms | 5K |
| Agentic Coding | A | Very compromised (needs ~6.7 GB host RAM) | 3.0 tok/s | 95307 ms | 5K |
| Reasoning | A | Runs with offload (needs ~2.8 GB host RAM) | 3.2 tok/s | 71458 ms | 5K |
| RAG | A | Very compromised (needs ~6.7 GB host RAM) | 3.0 tok/s |
Quantization options
How Pixtral Large 124B (124B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.4 GB | Low | S87 |
Q3_K_S | 3 | 60.8 GB | Low | S87 |
NVFP4Best for your GPU |
Get started
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server start