Can Gemma 2 27B run on NVIDIA GH200 96GB?

YES — Runs Great

A70Great

Estimated from fit model

Gemma 2 27B needs ~38.5 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~207 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) — 38.5 GB, 206.6 tok/s, Runs well

38.5 GB required96.0 GB available

40% VRAM used

Fit status

Runs well

Decode

206.6 tok/s

TTFT

937 ms

Safe context

Memory

38.5 GB / 96.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsGemma 2 27B 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: 206.6 tok/s decode · 937ms TTFT (warm) · 516 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	B	Runs well	206.6 tok/s	511 ms	8K
Coding	A	Runs well	206.6 tok/s	937 ms	8K
Agentic Coding	A	Runs well	206.6 tok/s	1363 ms	8K
Reasoning	A	Runs well	206.6 tok/s	1108 ms	8K
RAG	A	Runs well	206.6 tok/s	1704 ms	8K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B59
Q3_K_S	3	13.2 GB	Low	B59
NVFP4	4

Get started

Copy-paste commands to run Gemma 2 27B on your machine.

Run

ollama run gemma2:27b

Your hardware

More models your NVIDIA GH200 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can NVIDIA GH200 96GB run Gemma 2 27B?

Yes, NVIDIA GH200 96GB can run Gemma 2 27B with a A grade (Runs well). Expected decode speed: 206.6 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 38.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 27B?

The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 27B run at on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Gemma 2 27B achieves approximately 206.6 tokens per second decode speed with a time-to-first-token of 937ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA GH200 96GB receives a A grade with 206.6 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA GH200 96GB See all hardware for Gemma 2 27B

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