Pixtral 12B needs ~11.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~26 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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.5 GB host RAM)
Decode
26.4 tok/s
TTFT
7331 ms
Safe context
11K
Memory
11.8 GB / 11.0 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 41.9 tok/s | 2518 ms | 11K |
| Coding | B | Runs with offload (needs ~0.5 GB host RAM) | 26.4 tok/s | 7331 ms | 11K |
| Agentic Coding | F | Too heavy | 17.5 tok/s | 16135 ms | 11K |
| Reasoning | B | Runs with offload (needs ~0.5 GB host RAM) | 26.4 tok/s | 8664 ms | 11K |
| RAG | F | Too heavy | 17.5 tok/s | 20169 ms |
How Pixtral 12B (12B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | A76 |
Q3_K_S | 3 | 5.9 GB | Low | A76 |
NVFP4 | 4 |
Copy-paste commands to run Pixtral 12B on your machine.
Run
ollama run pixtralUpgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 30%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 50%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, GTX 1080 Ti 11GB can run Pixtral 12B with a B grade (Runs with offload (needs ~0.5 GB host RAM)). Expected decode speed: 26.4 tok/s.
Pixtral 12B (12B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Pixtral 12B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1080 Ti 11GB, Pixtral 12B achieves approximately 26.4 tokens per second decode speed with a time-to-first-token of 7331ms using Q4_K_M quantization.
For coding workloads, Pixtral 12B on GTX 1080 Ti 11GB receives a B grade with 26.4 tok/s and 11K context.
On GTX 1080 Ti 11GB, Pixtral 12B can safely use up to 11K 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-12b-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 11K |
| Medium |
| A76 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | A76 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 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.