Can Nemotron Mini 4B run on B100 192GB?

YES — Runs Great

C44Usable

○Estimated from fit model

Nemotron Mini 4B needs ~24.8 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 24.8 GB, 56.0 tok/s, Runs well

24.8 GB required192.0 GB available

13% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

Memory

24.8 GB / 192.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsNemotron Mini 4B on B100 192GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	C	Runs well	56.0 tok/s	1886 ms	4K
Coding	C	Runs well	56.0 tok/s	3457 ms	4K
Agentic Coding	C	Runs well	56.0 tok/s	5029 ms	4K
Reasoning	C	Runs well	56.0 tok/s	4086 ms	4K
RAG	C	Runs well	56.0 tok/s	6286 ms	4K

Quantization options

How Nemotron Mini 4B (4B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	D38
Q3_K_S	3	2.0 GB	Low	D38
NVFP4	4

Get started

Copy-paste commands to run Nemotron Mini 4B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "nvidia/Nemotron-Mini-4B-Instruct" \
 --hf-file "Nemotron-Mini-4B-Instruct-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Frequently asked questions

See all results for B100 192GB See all hardware for Nemotron Mini 4B

URL: https://willitrunai.com/can-run/nemotron-mini-4b-on-b100-192gb

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