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

YES — Runs Great

A84Great

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 ~62 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	61.9 tok/s	3130 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

Inference speed

Ministral 3 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Ministral 3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

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

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	Capabilities
Qwen 3.5 9B	9B	S	55 tok/s
Qwen 3 14B	14B	S	35.5 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: 61.9 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 61.9 tokens per second decode speed with a time-to-first-token of 3130ms 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 61.9 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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/ministral-3-8b-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: