Can Pixtral 12B run on RTX 3050 8GB?

YES — With Q2_K

B62Good

Estimated from fit model

Pixtral 12B needs ~9.1 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q2_K quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: 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.

Pixtral 12B at Q4_K_M needs 11.8 GB — too much for RTX 3050 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.8 GB, exceeds 8.0 GB available

11.8 GB required8.0 GB available

148% VRAM needed

3.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.2 tok/s

TTFT

26759 ms

Safe context

Memory

11.8 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsPixtral 12B on RTX 3050 8GB
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: 7.2 tok/s decode · 26.8s TTFT (warm) · 18 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.

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 0.6 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	F	Too heavy	9.1 tok/s	11589 ms	4K
Coding	F	Too heavy	7.2 tok/s	26759 ms	4K
Agentic Coding	F	Too heavy	4.9 tok/s	57893 ms	4K
Reasoning	F	Too heavy	7.2 tok/s	31624 ms	4K
RAG	F	Too heavy	4.9 tok/s	72366 ms	4K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	4.7 GB	Low	A77
Q3_K_S	3	5.9 GB	Low	F0
NVFP4	4	6.7 GB	Medium	F0
Q4_K_M	4	7.3 GB	Medium	F0
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

Upgrade options

Hardware that runs Pixtral 12B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+136)

Makes the model fit on the accelerator instead of staying completely out of reach.25.4 tok/s decode

Makes 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.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.40.8 tok/s decode

Makes 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.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.30.9 tok/s decode

Makes 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.

~$499 MSRP

Frequently asked questions

Can RTX 3050 8GB run Pixtral 12B?

Yes, RTX 3050 8GB can run Pixtral 12B at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 11.8 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 16.4 tok/s.

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 11.8 GB at Q4_K_M quantization. On RTX 3050 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for Pixtral 12B?

The recommended quantization is Q4_K_M, but on RTX 3050 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will Pixtral 12B run at on RTX 3050 8GB?

On RTX 3050 8GB, Pixtral 12B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11787ms using Q2_K quantization.

Can RTX 3050 8GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on RTX 3050 8GB receives a F grade with 7.2 tok/s and 4K context.

What context window can Pixtral 12B use on RTX 3050 8GB?

On RTX 3050 8GB, Pixtral 12B can safely use up to 9K tokens of context at Q2_K quantization. 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 RTX 3050 8GB?

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 RTX 3050 8GB See all hardware for Pixtral 12B

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Preview:

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.

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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

9.1 tok/s

11589 ms

Coding

Too heavy

7.2 tok/s

26759 ms

Agentic Coding

Too heavy

4.9 tok/s

57893 ms

Reasoning

Too heavy

7.2 tok/s

31624 ms

RAG

Too heavy

4.9 tok/s

72366 ms

Quant

Bits

VRAM

Quality

Fit

Q2_KBest for your GPU

4.7 GB

Low

A77

Q3_K_S

5.9 GB

Low

NVFP4

6.7 GB

Medium

Q4_K_M

7.3 GB

Medium

Q5_K_M

8.6 GB

High

Q6_K

9.8 GB

High

Q8_0

12.8 GB

Very High

F16

24.6 GB

Maximum

Frequently asked questions

Can RTX 3050 8GB run Pixtral 12B?

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 11.8 GB at Q4_K_M quantization. On RTX 3050 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for Pixtral 12B?

The recommended quantization is Q4_K_M, but on RTX 3050 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will Pixtral 12B run at on RTX 3050 8GB?

On RTX 3050 8GB, Pixtral 12B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11787ms using Q2_K quantization.

Can RTX 3050 8GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on RTX 3050 8GB receives a F grade with 7.2 tok/s and 4K context.

What context window can Pixtral 12B use on RTX 3050 8GB?

On RTX 3050 8GB, Pixtral 12B can safely use up to 9K tokens of context at Q2_K quantization. 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 RTX 3050 8GB?

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.