Can Pixtral 12B run on RTX 3080 Ti 12GB?

YES — With Offload

A77Great

○Estimated from fit model

Pixtral 12B needs ~11.9 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~97 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced

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) — 11.9 GB, 97.1 tok/s, Runs with offload

11.9 GB required12.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

97.1 tok/s

TTFT

1993 ms

Safe context

17K

Memory

11.9 GB / 12.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPixtral 12B on RTX 3080 Ti 12GB
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: 97.1 tok/s decode · 2.0s TTFT (warm) · 243 tok/s prefill

What limits this setup

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.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	97.1 tok/s	1087 ms	17K
Coding	A	Runs with offload	97.1 tok/s	1993 ms	17K
Agentic Coding	B	Very compromised (needs ~1.2 GB host RAM)	50.3 tok/s	5594 ms	17K
Reasoning	A	Runs with offload	97.1 tok/s	2356 ms	17K
RAG	B	Very compromised (needs ~1.2 GB host RAM)	50.3 tok/s	6992 ms	17K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A76
NVFP4	4	6.7 GB	Medium	A76
Q4_K_M	4	7.3 GB	Medium	A75
Q5_K_MBest for your GPU	5	8.6 GB	High	A75
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

Your hardware

More models your RTX 3080 Ti 12GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	A	52.3 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	A	41.5 tok/s
👁 Mistral Ministral 3 14B	14B	A	52.1 tok/s
👁 Microsoft Phi-4 14B	14B	A	47.3 tok/s
👁 Alibaba Qwen 2.5 14B	14B	A	48.4 tok/s

Frequently asked questions

See all results for RTX 3080 Ti 12GB See all hardware for Pixtral 12B

URL: https://willitrunai.com/can-run/pixtral-12b-on-rtx-3080-ti-12gb