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URL: https://willitrunai.com/can-run/mistral-small-24b-on-t4-16gb

⇱ Mistral Small 24B on NVIDIA T4 16GB? No — Alternatives

Can Mistral Small 24B run on NVIDIA T4 16GB?

YES — With NVFP4

B69Good
○Estimated from fit model

Mistral Small 24B needs ~18.7 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With NVFP4 quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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 24B at Q4_K_M needs 19.9 GB — too much for NVIDIA T4 16GB (16.0 GB). Runs at NVFP4 (18.7 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.9 GB, exceeds 16.0 GB available
19.9 GB required16.0 GB available
124% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.9 tok/s

TTFT

27999 ms

Safe context

4K

Memory

19.9 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB
KV Cache2.4 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 24B on NVIDIA T4 16GB
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: 6.9 tok/s decode · 28.0s TTFT (warm) · 17 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 10% 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~2.1 GB host RAM)7.9 tok/s13288 ms4K
CodingFToo heavy6.9 tok/s27999 ms4K
Agentic CodingFToo heavy5.4 tok/s52521 ms4K
ReasoningFToo heavy6.9 tok/s33090 ms4K
RAGFToo heavy5.4 tok/s65651 ms4K

Quantization options

How Mistral Small 24B (24B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowA83
Q3_K_SBest for your GPU
3
11.8 GB
LowA83
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 24B on your machine.

Run

ollama run mistral-small

Upgrade options

Hardware that runs Mistral Small 24B well

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick
20 GB VRAM (+4)360 GB/s (+40)
A
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

👁 NVIDIA
RTX 3090 24GBBest value
24 GB VRAM (+8)936 GB/s (+616)
A
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

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+8)672 GB/s (+352)
A
Makes the model fit on the accelerator instead of staying completely out of reach.41.4 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

See all results for NVIDIA T4 16GBSee all hardware for Mistral Small 24B