Pixtral Large 124B needs ~89.9 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~15 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
9.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~8.3 GB host RAM)
Decode
14.6 tok/s
TTFT
13301 ms
Safe context
4K
Memory
89.9 GB / 80.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 8.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~6.3 GB host RAM) | 15.5 tok/s | 6820 ms | 4K |
| Coding | A | Very compromised (needs ~8.3 GB host RAM) | 14.6 tok/s | 13301 ms | 4K |
| Agentic Coding | A | Very compromised (needs ~12.1 GB host RAM) | 12.9 tok/s | 21779 ms | 4K |
| Reasoning | A | Very compromised (needs ~8.3 GB host RAM) | 14.6 tok/s | 15720 ms | 4K |
| RAG | A | Very compromised (needs ~12.1 GB host RAM) | 12.9 tok/s |
How Pixtral Large 124B (124B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.4 GB | Low | S87 |
Q3_K_SBest for your GPU | 3 | 60.8 GB | Low | S87 |
Copy-paste commands to run Pixtral Large 124B on your machine.
Run
lms load Pixtral-Large-Instruct-2411 && lms server startYes, NVIDIA H100 PCIe 80GB can run Pixtral Large 124B with a A grade (Very compromised (needs ~8.3 GB host RAM)). Expected decode speed: 14.6 tok/s.
Pixtral Large 124B (124B parameters) requires approximately 89.9 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 H100 PCIe 80GB, Pixtral Large 124B achieves approximately 14.6 tokens per second decode speed with a time-to-first-token of 13301ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on NVIDIA H100 PCIe 80GB receives a A grade with 14.6 tok/s and 4K context.
On NVIDIA H100 PCIe 80GB, Pixtral Large 124B can safely use up to 4K 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-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 27223 ms |
| 4K |
| 4 |
69.4 GB |
| Medium |
| F0 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.