Can Mistral Small 3.2 24B Instruct 2506 run on RTX 4090 Laptop 16GB?

Q: What should I upgrade first if Mistral Small 3.2 24B Instruct 2506 feels slow on RTX 4090 Laptop 16GB?

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.

YES — With NVFP4

D33Poor

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~19.1 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With NVFP4 quantization, expect ~19 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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.

Mistral Small 3.2 24B Instruct 2506 at Q4_K_M needs 20.3 GB — too much for RTX 4090 Laptop 16GB (16.0 GB). Runs at NVFP4 (19.1 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.3 GB, exceeds 16.0 GB available

20.3 GB required16.0 GB available

127% VRAM needed

4.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.4 tok/s

TTFT

13469 ms

Safe context

Memory

20.3 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on RTX 4090 Laptop 16GB
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: 14.4 tok/s decode · 13.5s TTFT (warm) · 36 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 20% 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 2.2 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	D	Very compromised (needs ~2.2 GB host RAM)	16.7 tok/s	6314 ms	4K
Coding	F	Too heavy	14.4 tok/s	13469 ms	4K
Agentic Coding	F	Too heavy	10.9 tok/s	25760 ms	4K
Reasoning	F	Too heavy	14.4 tok/s	15918 ms	4K
RAG	F	Too heavy	10.9 tok/s	32200 ms	4K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C51
Q3_K_SBest for your GPU	3	11.8 GB	Low	C51

Get started

Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.

Run

lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server start

Upgrade options

Hardware that runs Mistral Small 3.2 24B Instruct 2506 well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.13.4 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.

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Makes the model fit on the accelerator instead of staying completely out of reach.44.8 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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.52.3 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run Mistral Small 3.2 24B Instruct 2506?

Yes, RTX 4090 Laptop 16GB can run Mistral Small 3.2 24B Instruct 2506 at NVFP4 quantization (Very compromised (needs ~2.2 GB host RAM)). The recommended Q4_K_M requires 20.3 GB which exceeds available memory, but at NVFP4 it needs only 19.1 GB. Expected decode speed: 18.7 tok/s.

How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?

Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 20.3 GB at Q4_K_M quantization. On RTX 4090 Laptop 16GB, it fits at NVFP4 using 19.1 GB.

What is the best quantization for Mistral Small 3.2 24B Instruct 2506?

The recommended quantization is Q4_K_M, but on RTX 4090 Laptop 16GB the best fitting quantization is NVFP4, which uses 19.1 GB.

What speed will Mistral Small 3.2 24B Instruct 2506 run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10355ms using NVFP4 quantization.

Can RTX 4090 Laptop 16GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on RTX 4090 Laptop 16GB receives a F grade with 14.4 tok/s and 4K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on RTX 4090 Laptop 16GB?

See all results for RTX 4090 Laptop 16GB See all hardware for Mistral Small 3.2 24B Instruct 2506

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