Pixtral Large 124B needs ~109.6 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~7 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
8.0 tok/s
TTFT
24179 ms
Safe context
131K
Memory
109.6 GB / 184.3 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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 8.0 tok/s | 13188 ms | 131K |
| Coding | S | Runs well | 7.4 tok/s | 26294 ms | 131K |
| Agentic Coding | S | Runs well | 8.0 tok/s | 35169 ms | 131K |
| Reasoning | S | Runs well | 8.0 tok/s | 28575 ms | 131K |
| RAG | S | Runs well | 8.0 tok/s | 43961 ms | 131K |
How Pixtral Large 124B (124B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.4 GB | Low | A81 |
Q3_K_S | 3 | 60.8 GB | Low | A83 |
NVFP4 | 4 |
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 DeepSeek V4 Flash | 284B | S | 17.8 tok/s |
Yes, Mac Studio M3 Ultra 256GB can run Pixtral Large 124B with a S grade (Runs well). Expected decode speed: 7.4 tok/s.
Pixtral Large 124B (124B parameters) requires approximately 109.6 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 Mac Studio M3 Ultra 256GB, Pixtral Large 124B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26294ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on Mac Studio M3 Ultra 256GB receives a S grade with 7.4 tok/s and 131K context.
On Mac Studio M3 Ultra 256GB, Pixtral Large 124B can safely use up to 131K 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-m3-ultra-256gb" 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 |
| A84 |
Q4_K_M | 4 | 75.6 GB | Medium | A84 |
Q5_K_M | 5 | 89.3 GB | High | S86 |
Q6_K | 6 | 101.7 GB | High | S87 |
Q8_0Best for your GPU | 8 | 132.7 GB | Very High | S87 |
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. Mac Studio M3 Ultra 256GB 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.