Can Mistral Small 3.1 24B run on RTX 4500 Ada 24GB?

YES — Tight Fit

A81Great

Estimated from fit model

Mistral Small 3.1 24B needs ~20.7 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 20.7 GB, 25.1 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

25.1 tok/s

TTFT

7726 ms

Safe context

38K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Small 3.1 24B on RTX 4500 Ada 24GB
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: 25.1 tok/s decode · 7.7s TTFT (warm) · 63 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	25.1 tok/s	4214 ms	38K
Coding	A	Tight fit	25.1 tok/s	7726 ms	38K
Agentic Coding	A	Runs with offload	25.1 tok/s	11237 ms	38K
Reasoning	A	Tight fit	25.1 tok/s	9130 ms	38K
RAG	A	Runs with offload	25.1 tok/s	14046 ms	38K

Quantization options

How Mistral Small 3.1 24B (24B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A80
Q3_K_S	3	11.8 GB	Low	A81
NVFP4	4

Get started

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

Run

ollama run mistral-small:24b

Your hardware

More models your RTX 4500 Ada 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.6 tok/s
Qwen 3.5 27B	27B	S	22.4 tok/s

Frequently asked questions

Can RTX 4500 Ada 24GB run Mistral Small 3.1 24B?

Yes, RTX 4500 Ada 24GB can run Mistral Small 3.1 24B with a A grade (Tight fit). Expected decode speed: 25.1 tok/s.

How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B (24B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Mistral Small 3.1 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.1 24B run at on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Mistral Small 3.1 24B achieves approximately 25.1 tokens per second decode speed with a time-to-first-token of 7726ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Mistral Small 3.1 24B for coding?

For coding workloads, Mistral Small 3.1 24B on RTX 4500 Ada 24GB receives a A grade with 25.1 tok/s and 38K context.

What context window can Mistral Small 3.1 24B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Mistral Small 3.1 24B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4500 Ada 24GB See all hardware for Mistral Small 3.1 24B

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