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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf-on-a40-48gb

⇱ Mistral Small 24B Instruct 2501 on NVIDIA A40 48GB? YES

Can Mistral Small 24B Instruct 2501 run on NVIDIA A40 48GB?

YES — Runs Great

C50Usable
○Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~23.5 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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) — 23.5 GB, 37.1 tok/s, Runs well
23.5 GB required48.0 GB available
49% VRAM used

Fit status

Runs well

Decode

37.1 tok/s

TTFT

5221 ms

Safe context

156K

Memory

23.5 GB / 48.0 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsMistral Small 24B Instruct 2501 on NVIDIA A40 48GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 37.1 tok/s decode · 5.2s TTFT (warm) · 93 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
ChatCRuns well37.1 tok/s2848 ms156K
CodingCRuns well37.1 tok/s5221 ms156K
Agentic CodingCRuns well37.1 tok/s7594 ms156K
ReasoningCRuns well37.1 tok/s6170 ms156K
RAGCRuns well37.1 tok/s9492 ms156K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC43
Q3_K_S
3
11.8 GB
LowC44
NVFP4
4
13.4 GB
MediumC44
Q4_K_M
4
14.6 GB
MediumC44
Q5_K_M
5
17.3 GB
HighC45
Q6_K
6
19.7 GB
HighC46
Q8_0Best for your GPU
8
25.7 GB
Very HighC48
F16
16
49.2 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server start

Upgrade options

Hardware that runs Mistral Small 24B Instruct 2501 well

AMD Instinct MI210 64GBBudget pick
64 GB VRAM (+16)1638 GB/s (+942)
C
Raises estimated decode speed by about 105%.76.1 tok/s decode

Raises estimated decode speed by about 105%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

See all results for NVIDIA A40 48GBSee all hardware for Mistral Small 24B Instruct 2501