Can Pixtral 12B run on GTX 1080 Ti 11GB?

YES — With Offload

B63Good

Estimated from fit model

Pixtral 12B needs ~11.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.8 GB, 26.4 tok/s, Runs with offload (needs ~0.5 GB host RAM)

11.8 GB required11.0 GB available

107% VRAM needed

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%

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsPixtral 12B on GTX 1080 Ti 11GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 26.4 tok/s decode · 7.3s TTFT (warm) · 66 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

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	11K

Quantization options

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	6.7 GB	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

Get started

Copy-paste commands to run Pixtral 12B on your machine.

Run

ollama run pixtral

Opciones de mejora

Hardware que ejecuta bien Pixtral 12B

RTX 3060 12GBOpción económica

12 GB VRAM (+1)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.34.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 30%.

~$329 MSRP

RTX 5060 Ti 16GBMejor relación calidad-precio

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.39.6 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 50%.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+5)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.32.4 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Pixtral 12B?

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.

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Pixtral 12B?

The recommended quantization for Pixtral 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Pixtral 12B run at on GTX 1080 Ti 11GB?

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.

Can GTX 1080 Ti 11GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on GTX 1080 Ti 11GB receives a B grade with 26.4 tok/s and 11K context.

What context window can Pixtral 12B use on GTX 1080 Ti 11GB?

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.

What should I upgrade first if Pixtral 12B feels slow on GTX 1080 Ti 11GB?

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.

See all results for GTX 1080 Ti 11GB See all hardware for Pixtral 12B

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