Can Ministral 8B run on RTX A4000 16GB?

YES — Runs Great

B64Good

○Estimated from fit model

Ministral 8B needs ~9.9 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~69 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.9 GB, 69.1 tok/s, Runs well

9.9 GB required16.0 GB available

62% VRAM used

Fit status

Runs well

Decode

69.1 tok/s

TTFT

2802 ms

Safe context

61K

Memory

9.9 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMinistral 8B on RTX A4000 16GB
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: 69.1 tok/s decode · 2.8s TTFT (warm) · 173 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	B	Runs well	69.1 tok/s	1529 ms	61K
Coding	B	Runs well	69.1 tok/s	2802 ms	61K
Agentic Coding	B	Runs well	69.1 tok/s	4076 ms	61K
Reasoning	B	Runs well	69.1 tok/s	3312 ms	61K
RAG	B	Runs well	69.1 tok/s	5095 ms	61K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B57
Q3_K_S	3	3.9 GB	Low	B57
NVFP4	4	4.5 GB	Medium	B58
Q4_K_M	4	4.9 GB	Medium	B58
Q5_K_M	5	5.8 GB	High	B59
Q6_K	6	6.6 GB	High	B60
Q8_0Best for your GPU	8	8.6 GB	Very High	B61
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run ministral

Frequently asked questions

See all results for RTX A4000 16GB See all hardware for Ministral 8B

URL: https://willitrunai.com/can-run/ministral-8b-on-a4000-16gb