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⇱ Mistral Small 3.2 24B on RTX 4500 Ada 24GB? TIGHT FIT

Can Mistral Small 3.2 24B run on RTX 4500 Ada 24GB?

YES — Tight Fit

A84Great
○Estimated from fit model

Mistral Small 3.2 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
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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.

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 3.2 24B on RTX 4500 Ada 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
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

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well25.1 tok/s4214 ms38K
CodingATight fit25.1 tok/s7726 ms38K
Agentic CodingARuns with offload25.1 tok/s11237 ms38K
ReasoningATight fit25.1 tok/s9130 ms38K
RAGARuns with offload25.1 tok/s14046 ms38K

Quantization options

How Mistral Small 3.2 24B (24B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA83
Q3_K_S
3
11.8 GB
LowA84
NVFP4
4
13.4 GB
MediumA84
Q4_K_M
4
14.6 GB
MediumA84
Q5_K_MBest for your GPU
5
17.3 GB
HighA83
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 on your machine.

Run

ollama run mistral-small3.2

Your hardware

More models your RTX 4500 Ada 24GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS51.6 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS22.4 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS22.4 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS53.4 tok/s
👁 Alibaba
Qwen 3.5 35B A3B		35BA28.9 tok/s

Frequently asked questions

See all results for RTX 4500 Ada 24GBSee all hardware for Mistral Small 3.2 24B