Can Ministral 3 14B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

A83Great

Estimated from fit model

Ministral 3 14B needs ~21.4 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~157 tok/s.

Runtime: vLLMCapacity: RoomyBandwidth: HighStack: 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) — 21.4 GB, 169.2 tok/s, Runs well

21.4 GB required80.0 GB available

27% VRAM used

Fit status

Runs well

Decode

169.2 tok/s

TTFT

1144 ms

Safe context

262K

Memory

21.4 GB / 80.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime2.4 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsMinistral 3 14B on NVIDIA H100 PCIe 80GB
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: 169.2 tok/s decode · 1.1s TTFT (warm) · 423 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	169.2 tok/s	624 ms	262K
Coding	A	Runs well	157.4 tok/s	1230 ms	262K
Agentic Coding	A	Runs well	169.2 tok/s	1664 ms	262K
Reasoning	A	Runs well	169.2 tok/s	1352 ms	262K
RAG	A	Runs well	169.2 tok/s	2081 ms	262K

Quantization options

How Ministral 3 14B (14B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A75
Q3_K_S	3	6.9 GB	Low	A75
NVFP4	4

Get started

Copy-paste commands to run Ministral 3 14B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Ministral-3-14B-Instruct-2512" \
  --hf-file "Ministral-3-14B-Instruct-2512-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	193.1 tok/s
Qwen 3.5 27B	27B	S	88.1 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Ministral 3 14B?

Yes, NVIDIA H100 PCIe 80GB can run Ministral 3 14B with a A grade (Runs well). Expected decode speed: 157.4 tok/s.

How much VRAM does Ministral 3 14B need?

Ministral 3 14B (14B parameters) requires approximately 21.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 3 14B?

The recommended quantization for Ministral 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 3 14B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Ministral 3 14B achieves approximately 157.4 tokens per second decode speed with a time-to-first-token of 1230ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Ministral 3 14B for coding?

For coding workloads, Ministral 3 14B on NVIDIA H100 PCIe 80GB receives a A grade with 157.4 tok/s and 262K context.

What context window can Ministral 3 14B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Ministral 3 14B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Ministral 3 14B

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