Can Pixtral 12B run on RTX 5000 Ada 32GB?

YES — Runs Great

A74Great

○Estimated from fit model

Pixtral 12B needs ~14.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 14.2 GB, 67.7 tok/s, Runs well

14.2 GB required32.0 GB available

44% VRAM used

Fit status

Runs well

Decode

67.7 tok/s

TTFT

2861 ms

Safe context

131K

Memory

14.2 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsPixtral 12B on RTX 5000 Ada 32GB
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: 67.7 tok/s decode · 2.9s TTFT (warm) · 169 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	67.7 tok/s	1560 ms	131K
Coding	A	Runs well	67.7 tok/s	2861 ms	131K
Agentic Coding	A	Runs well	67.7 tok/s	4161 ms	131K
Reasoning	A	Runs well	67.7 tok/s	3381 ms	131K
RAG	A	Runs well	67.7 tok/s	5202 ms	131K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B67
Q3_K_S	3	5.9 GB	Low	B68
NVFP4	4	6.7 GB	Medium	B68
Q4_K_M	4	7.3 GB	Medium	B68
Q5_K_M	5	8.6 GB	High	B69
Q6_K	6	9.8 GB	High	B69
Q8_0	8	12.8 GB	Very High	A71
F16Best for your GPU	16	24.6 GB	Maximum	A72

Get started

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

Run

ollama run pixtral

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.2 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.3 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	58.6 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.1 tok/s

Frequently asked questions

See all results for RTX 5000 Ada 32GB See all hardware for Pixtral 12B

URL: https://willitrunai.com/can-run/pixtral-12b-on-rtx-5000-ada-32gb