Pixtral Large 124B needs ~91.5 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~47 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 with offload
Decode
46.6 tok/s
TTFT
4156 ms
Safe context
29K
Memory
91.5 GB / 96.0 GB
This setup is broadly balanced for this model.
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.
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 | Tight fit | 46.6 tok/s | 2267 ms | 29K |
| Coding | S | Runs with offload | 46.6 tok/s | 4156 ms | 29K |
| Agentic Coding | S | Runs with offload | 36.0 tok/s | 7825 ms | 29K |
| Reasoning | S | Runs with offload | 46.6 tok/s | 4912 ms | 29K |
| RAG | S | Runs with offload (needs ~0.7 GB host RAM) | 39.1 tok/s | 8994 ms | 29K |
How Pixtral Large 124B (124B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.4 GB | Low | S87 |
Q3_K_S | 3 | 60.8 GB | Low | S87 |
NVFP4 | 4 |
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server startYes, NVIDIA H20 96GB can run Pixtral Large 124B with a S grade (Runs with offload). Expected decode speed: 46.6 tok/s.
Pixtral Large 124B (124B parameters) requires approximately 91.5 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 NVIDIA H20 96GB, Pixtral Large 124B achieves approximately 46.6 tokens per second decode speed with a time-to-first-token of 4156ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on NVIDIA H20 96GB receives a S grade with 46.6 tok/s and 29K context.
On NVIDIA H20 96GB, Pixtral Large 124B can safely use up to 29K 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-h20-96gb" 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_MBest for your GPU | 4 | 75.6 GB | Medium | S87 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.