Pixtral Large 124B needs ~95.7 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~4 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
3.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~2.8 GB host RAM)
Decode
8.0 tok/s
TTFT
24162 ms
Safe context
5K
Memory
95.7 GB / 92.2 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload (needs ~0.7 GB host RAM) | 8.4 tok/s | 12533 ms | 5K |
| Coding | A | Runs with offload | 4.2 tok/s | 45879 ms | 5K |
| Agentic Coding | A | Very compromised (needs ~6.7 GB host RAM) | 7.4 tok/s | 38085 ms | 5K |
| Reasoning | S | Runs with offload (needs ~2.8 GB host RAM) | 8.0 tok/s | 28555 ms | 5K |
| RAG | A | Very compromised (needs ~6.7 GB host RAM) | 7.4 tok/s |
How Pixtral Large 124B (124B params) fits at each quantization level on MacBook Pro M4 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 |
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server startYes, MacBook Pro M4 Max 128GB can run Pixtral Large 124B with a A grade (Runs with offload). Expected decode speed: 4.2 tok/s.
Pixtral Large 124B (124B parameters) requires approximately 95.7 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 MacBook Pro M4 Max 128GB, Pixtral Large 124B achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 45879ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on MacBook Pro M4 Max 128GB receives a A grade with 4.2 tok/s and 5K context.
On MacBook Pro M4 Max 128GB, Pixtral Large 124B can safely use up to 5K 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-m4-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 47607 ms |
| 5K |
| 4 |
69.4 GB |
| Medium |
| S87 |
Q4_K_M | 4 | 75.6 GB | Medium | F0 |
Q5_K_M | 5 | 89.3 GB | High | F0 |
Q6_K | 6 | 101.7 GB | High | F0 |
Q8_0 | 8 | 132.7 GB | Very High | F0 |
F16 | 16 | 254.2 GB | Maximum | F0 |
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.
Not always. MacBook Pro M4 Max 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.