Will It Run AI

Can Mistral Small 3.2 24B Instruct 2506 run on RTX 6000 Ada Laptop 16GB?

YES — With NVFP4

D33Poor
Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~19.1 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With NVFP4 quantization, expect ~17 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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 6000 Ada 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

13.1 tok/s

TTFT

14761 ms

Safe context

4K

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 6000 Ada 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: 13.1 tok/s decode · 14.8s TTFT (warm) · 33 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

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~2.2 GB host RAM)15.3 tok/s6919 ms4K
CodingFToo heavy13.1 tok/s14761 ms4K
Agentic CodingFToo heavy10.0 tok/s28230 ms4K
ReasoningFToo heavy13.1 tok/s17444 ms4K
RAGFToo heavy10.0 tok/s35288 ms4K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC51
Q3_K_SBest for your GPU
3
11.8 GB
LowC51
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

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

アップグレードオプション

Mistral Small 3.2 24B Instruct 2506を快適に動かすハードウェア

NVIDIARTX 4000 Ada 20GBコスパ重視
20 GB VRAM (+4)
C
Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/sデコード

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

NVIDIARTX 3090 24GBコスパ最良
24 GB VRAM (+8)936 GB/s (+360)
C
Makes the model fit on the accelerator instead of staying completely out of reach.44.8 tok/sデコード

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

NVIDIARTX 4090 24GBNVIDIAアップグレード
24 GB VRAM (+8)1008 GB/s (+432)
C
Makes the model fit on the accelerator instead of staying completely out of reach.52.3 tok/sデコード

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 6000 Ada Laptop 16GB run Mistral Small 3.2 24B Instruct 2506?

Yes, RTX 6000 Ada 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: 17.1 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 6000 Ada 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 6000 Ada 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 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 17.1 tokens per second decode speed with a time-to-first-token of 11348ms using NVFP4 quantization.

Can RTX 6000 Ada Laptop 16GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on RTX 6000 Ada Laptop 16GB receives a F grade with 13.1 tok/s and 4K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Small 3.2 24B Instruct 2506 feels slow on RTX 6000 Ada 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.

See all results for RTX 6000 Ada Laptop 16GBSee all hardware for Mistral Small 3.2 24B Instruct 2506
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