Can Ministral 3 8B run on RTX 4000 Ada 20GB?

YES — Runs Great

A83Great

Estimated from fit model

Ministral 3 8B needs ~11.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 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) — 11.7 GB, 61.9 tok/s, Runs well

11.7 GB required20.0 GB available

59% VRAM used

Fit status

Runs well

Decode

61.9 tok/s

TTFT

3130 ms

Safe context

77K

Memory

11.7 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime2.6 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsMinistral 3 8B on RTX 4000 Ada 20GB
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: 61.9 tok/s decode · 3.1s TTFT (warm) · 155 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	61.9 tok/s	1707 ms	77K
Coding	A	Runs well	57.5 tok/s	3365 ms	77K
Agentic Coding	S	Runs well	61.9 tok/s	4552 ms	77K
Reasoning	A	Runs well	61.9 tok/s	3699 ms	77K
RAG	S	Runs well	61.9 tok/s	5691 ms	77K

Quantization options

How Ministral 3 8B (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A76
Q3_K_S	3	3.9 GB	Low	A77
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A78
Q6_K	6	6.6 GB	High	A79
Q8_0Best for your GPU	8	8.6 GB	Very High	A80
F16	16	16.4 GB	Maximum	F0

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 RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	55 tok/s
Qwen 3 14B	14B	S	35.5 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	33.7 tok/s
Ministral 3 14B	14B	S	35.3 tok/s
CogVLM2 19B	19B	A	26 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Ministral 3 8B?

Yes, RTX 4000 Ada 20GB can run Ministral 3 8B with a A grade (Runs well). Expected decode speed: 57.5 tok/s.

How much VRAM does Ministral 3 8B need?

Ministral 3 8B (8B parameters) requires approximately 11.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 8B?

The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 8B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Ministral 3 8B achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Ministral 3 8B for coding?

For coding workloads, Ministral 3 8B on RTX 4000 Ada 20GB receives a A grade with 57.5 tok/s and 77K context.

What context window can Ministral 3 8B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Ministral 3 8B can safely use up to 77K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Ministral 3 8B

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