Pixtral Large 124B needs ~86.7 GB but RTX 6000 Ada 48GB only has 48.0 GB. Try a smaller quantization or lighter model.
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
38.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
79198 ms
Safe context
4K
Memory
86.7 GB / 48.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 86.7 GB, but this setup only exposes 48.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.6 tok/s | 40431 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 86128 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 130733 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 93598 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 163416 ms | 4K |
How Pixtral Large 124B (124B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 48.4 GB | Low | F0 |
Q3_K_S | 3 | 60.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$12,000 MSRP
No, Pixtral Large 124B requires more memory than RTX 6000 Ada 48GB provides.
Pixtral Large 124B (124B parameters) requires approximately 86.7 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 RTX 6000 Ada 48GB, Pixtral Large 124B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 86128ms using Q4_K_M quantization.
For coding workloads, Pixtral Large 124B on RTX 6000 Ada 48GB receives a F grade with 2.2 tok/s and 4K context.
On RTX 6000 Ada 48GB, 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-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
