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

YES — Tight Fit

A82Great

Estimated from fit model

Mistral Small 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

33K

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 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	33K
Coding	A	Tight fit	25.1 tok/s	7726 ms	33K
Agentic Coding	A	Runs with offload	25.1 tok/s	11237 ms	33K
Reasoning	A	Tight fit	25.1 tok/s	9130 ms	33K
RAG	A	Runs with offload	25.1 tok/s	14046 ms	33K

Inference speed

Mistral Small 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Mistral Small 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Mistral Small 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	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_MBest for your GPU	5	17.3 GB	High	A81
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

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

Run

ollama run mistral-small

Your hardware

More models your RTX 4500 Ada 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.6 tok/s
Qwen 3.5 27B	27B	S	22.4 tok/s
Qwen 3.6 27B	27B	S	22.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.4 tok/s
Qwen 3.5 35B A3B	35B	A	28.9 tok/s

Frequently asked questions

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

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

How much VRAM does Mistral Small 24B need?

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

What is the best quantization for Mistral Small 24B?

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

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

On RTX 4500 Ada 24GB, Mistral Small 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 24B for coding?

For coding workloads, Mistral Small 24B on RTX 4500 Ada 24GB receives a A grade with 25.1 tok/s and 33K context.

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

On RTX 4500 Ada 24GB, Mistral Small 24B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

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

Embed this result▼

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<iframe src="https://willitrunai.com/embed/mistral-small-24b-on-rtx-4500-ada-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

25.1 tok/s

4214 ms

33K

Coding

Tight fit

25.1 tok/s

7726 ms

33K

Agentic Coding

Runs with offload

25.1 tok/s

11237 ms

33K

Reasoning

Tight fit

25.1 tok/s

9130 ms

33K

RAG

Runs with offload

25.1 tok/s

14046 ms

33K

Mistral Small 24B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

88.2

Fits

RTX 4090 24GB

24 GB

Q4_K_M

56.3

Tight

RX 7900 XTX 24GB

24 GB

Q4_K_M

50.8

Tight

RTX 3090 24GB

24 GB

Q4_K_M

48.1

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

40.9

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

36.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

36.8

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

34.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

32.3

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

23.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

21.3

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

17.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

16.2

Fits

RTX 4070 12GB

12 GB

Q4_K_M

7.5

Too big

RTX 3060 12GB

12 GB

Q4_K_M

4.7

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.2

Too big

Quantization options

How Mistral Small 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	A81
Q3_K_S	3	11.8 GB	Low	A82
NVFP4	4	13.4 GB	Medium	A82
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_MBest for your GPU	5	17.3 GB	High	A81
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

51.6 tok/s

Qwen 3.5 27B

27B

22.4 tok/s

Qwen 3.6 27B

27B

22.4 tok/s

Qwen3-VL 30B A3B Instruct

30B

53.4 tok/s

Qwen 3.5 35B A3B

35B

28.9 tok/s

Frequently asked questions

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

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

How much VRAM does Mistral Small 24B need?

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

What is the best quantization for Mistral Small 24B?

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

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

On RTX 4500 Ada 24GB, Mistral Small 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 24B for coding?

For coding workloads, Mistral Small 24B on RTX 4500 Ada 24GB receives a A grade with 25.1 tok/s and 33K context.

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

On RTX 4500 Ada 24GB, Mistral Small 24B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.