Can Gemma 2 27B run on NVIDIA B200 180GB?

YES — Runs Great

B67Good

Estimated from fit model

Gemma 2 27B needs ~46.9 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~378 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) — 46.9 GB, 378.0 tok/s, Runs well

46.9 GB required180.0 GB available

26% VRAM used

Fit status

Runs well

Decode

378.0 tok/s

TTFT

512 ms

Safe context

Memory

46.9 GB / 180.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsGemma 2 27B on NVIDIA B200 180GB
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: 378.0 tok/s decode · 512ms TTFT (warm) · 945 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	378.0 tok/s	350 ms	8K
Coding	B	Runs well	378.0 tok/s	512 ms	8K
Agentic Coding	B	Runs well	378.0 tok/s	745 ms	8K
Reasoning	B	Runs well	378.0 tok/s	605 ms	8K
RAG	B	Runs well	378.0 tok/s	931 ms	8K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B57
Q3_K_S	3	13.2 GB	Low	B57
NVFP4	4	15.1 GB	Medium	B57
Q4_K_M	4	16.5 GB	Medium	B57
Q5_K_M	5	19.4 GB	High	B57
Q6_K	6	22.1 GB	High	B57
Q8_0	8	28.9 GB	Very High	B58
F16Best for your GPU	16	55.4 GB	Maximum	B61

Get started

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

Run

ollama run gemma2:27b

Frequently asked questions

Can NVIDIA B200 180GB run Gemma 2 27B?

Yes, NVIDIA B200 180GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 378.0 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 46.9 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 B200 180GB?

On NVIDIA B200 180GB, Gemma 2 27B achieves approximately 378.0 tokens per second decode speed with a time-to-first-token of 512ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA B200 180GB receives a B grade with 378.0 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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 B200 180GB See all hardware for Gemma 2 27B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

378.0 tok/s

350 ms

Coding

Runs well

378.0 tok/s

512 ms

Agentic Coding

Runs well

378.0 tok/s

745 ms

Reasoning

Runs well

378.0 tok/s

605 ms

RAG

Runs well

378.0 tok/s

931 ms

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B57
Q3_K_S	3	13.2 GB	Low	B57
NVFP4	4	15.1 GB	Medium	B57
Q4_K_M	4	16.5 GB	Medium	B57
Q5_K_M	5	19.4 GB	High	B57
Q6_K	6	22.1 GB	High	B57
Q8_0	8	28.9 GB	Very High	B58
F16Best for your GPU	16	55.4 GB	Maximum	B61

Frequently asked questions

Can NVIDIA B200 180GB run Gemma 2 27B?

Yes, NVIDIA B200 180GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 378.0 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 46.9 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 B200 180GB?

On NVIDIA B200 180GB, Gemma 2 27B achieves approximately 378.0 tokens per second decode speed with a time-to-first-token of 512ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA B200 180GB receives a B grade with 378.0 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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.