Can Mistral Small 3.1 24B run on RTX 4000 Ada Laptop 12GB?

Q: What should I upgrade first if Mistral Small 3.1 24B feels slow on RTX 4000 Ada Laptop 12GB?

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 Q2_K

B64Good

Estimated from fit model

Mistral Small 3.1 24B needs ~14.2 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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.

Mistral Small 3.1 24B at Q4_K_M needs 19.5 GB — too much for RTX 4000 Ada Laptop 12GB (12.0 GB). Runs at Q2_K (14.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.5 GB, exceeds 12.0 GB available

19.5 GB required12.0 GB available

163% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.3 tok/s

TTFT

30913 ms

Safe context

Memory

19.5 GB / 12.0 GB

Offload

40%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMistral Small 3.1 24B on RTX 4000 Ada Laptop 12GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 6.3 tok/s decode · 30.9s TTFT (warm) · 16 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 1.5 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	7.2 tok/s	14715 ms	4K
Coding	F	Too heavy	5.8 tok/s	33232 ms	4K
Agentic Coding	F	Too heavy	4.9 tok/s	57651 ms	4K
Reasoning	F	Too heavy	6.3 tok/s	36534 ms	4K
RAG	F	Too heavy	4.9 tok/s	72064 ms	4K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	F0
Q3_K_S	3	11.8 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 3.1 24B on your machine.

Run

ollama run mistral-small:24b

Upgrade options

Hardware that runs Mistral Small 3.1 24B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.15 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 (+12)936 GB/s (+504)

Makes the model fit on the accelerator instead of staying completely out of reach.48.1 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 (+12)1008 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.56.3 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 4000 Ada Laptop 12GB run Mistral Small 3.1 24B?

Yes, RTX 4000 Ada Laptop 12GB can run Mistral Small 3.1 24B at Q2_K quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 19.5 GB which exceeds available memory, but at Q2_K it needs only 14.2 GB. Expected decode speed: 16.2 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 19.5 GB at Q4_K_M quantization. On RTX 4000 Ada Laptop 12GB, it fits at Q2_K using 14.2 GB.

What is the best quantization for Mistral Small 3.1 24B?

The recommended quantization is Q4_K_M, but on RTX 4000 Ada Laptop 12GB the best fitting quantization is Q2_K, which uses 14.2 GB.

What speed will Mistral Small 3.1 24B run at on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Mistral Small 3.1 24B achieves approximately 16.2 tokens per second decode speed with a time-to-first-token of 11953ms using Q2_K quantization.

Can RTX 4000 Ada Laptop 12GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on RTX 4000 Ada Laptop 12GB receives a F grade with 5.8 tok/s and 4K context.

What context window can Mistral Small 3.1 24B use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Mistral Small 3.1 24B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB See all hardware for Mistral Small 3.1 24B

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