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URL: https://willitrunai.com/can-run/nemotron-70b-on-gh200-96gb


Can Nemotron 70B run on NVIDIA GH200 96GB?

YES — Runs Great

A76Great
Estimated from fit model

Nemotron 70B needs ~58.1 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~76 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 58.1 GB, 82.5 tok/s, Runs well
58.1 GB required96.0 GB available
61% VRAM used

Fit status

Runs well

Decode

82.5 tok/s

TTFT

2346 ms

Safe context

131K

Memory

58.1 GB / 96.0 GB

Memory breakdown

Weights42.7 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom9.6 GB

See how fast it feels

See how fast it feelsNemotron 70B on NVIDIA GH200 96GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 82.5 tok/s decode · 2.3s TTFT (warm) · 206 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well75.9 tok/s1392 ms131K
CodingARuns well75.9 tok/s2551 ms131K
Agentic CodingARuns well75.9 tok/s3711 ms131K
ReasoningARuns well75.9 tok/s3015 ms131K
RAGARuns well75.9 tok/s4639 ms131K

Quantization options

How Nemotron 70B (70B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowB64
Q3_K_S
3
34.3 GB
LowB65
NVFP4
4

Get started

Copy-paste commands to run Nemotron 70B on your machine.

Run

ollama run nemotron

Your hardware

More models your NVIDIA GH200 96GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS47 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for NVIDIA GH200 96GBSee all hardware for Nemotron 70B
39.2 GB
Medium
B67
Q4_K_M
4
42.7 GB
MediumB67
Q5_K_M
5
50.4 GB
HighB69
Q6_K
6
57.4 GB
HighB69
Q8_0Best for your GPU
8
74.9 GB
Very HighB69
F16
16
143.5 GB
MaximumF0
130.3 tok/s
👁 Mistral
Mistral Small 4 119B
119BS141.2 tok/s
👁 OpenAI
GPT-OSS 120B
117BS49.4 tok/s
👁 Cohere
Command A 111B
111BS52.2 tok/s