Can Ministral 3 8B run on NVIDIA L4 24GB?

YES — Runs Great

A81Great

○Estimated from fit model

Ministral 3 8B needs ~12.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: LowStack: OptimizedBottleneck: 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) — 12.1 GB, 43.0 tok/s, Runs well

12.1 GB required24.0 GB available

50% VRAM used

Fit status

Runs well

Decode

43.0 tok/s

TTFT

4507 ms

Safe context

103K

Memory

12.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on NVIDIA L4 24GB
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: 43.0 tok/s decode · 4.5s TTFT (warm) · 107 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	43.0 tok/s	2458 ms	103K
Coding	A	Runs well	43.0 tok/s	4507 ms	103K
Agentic Coding	A	Runs well	43.0 tok/s	6556 ms	103K
Reasoning	A	Runs well	43.0 tok/s	5326 ms	103K
RAG	A	Runs well	43.0 tok/s	8194 ms	103K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A75
Q3_K_S	3	3.9 GB	Low	A75
NVFP4	4

Get started

Copy-paste commands to run Ministral 3 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "mistralai/Ministral-3-8B-Instruct-2512" \
 --hf-file "Ministral-3-8B-Instruct-2512-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your NVIDIA L4 24GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 27B	27B	S	12.8 tok/s
👁 Alibaba

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for Ministral 3 8B

URL: https://willitrunai.com/can-run/ministral-3-8b-on-l4-24gb

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