Can Gemma 2 27B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

B68Good

Estimated from fit model

Gemma 2 27B needs ~43.0 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~257 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) — 43.0 GB, 257.0 tok/s, Runs well

43.0 GB required141.0 GB available

30% VRAM used

Fit status

Runs well

Decode

257.0 tok/s

TTFT

753 ms

Safe context

Memory

43.0 GB / 141.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsGemma 2 27B on NVIDIA H200 PCIe 141GB
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: 257.0 tok/s decode · 753ms TTFT (warm) · 643 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	257.0 tok/s	411 ms	8K
Coding	B	Runs well	257.0 tok/s	753 ms	8K
Agentic Coding	B	Runs well	257.0 tok/s	1096 ms	8K
Reasoning	B	Runs well	257.0 tok/s	890 ms	8K
RAG	B	Runs well	257.0 tok/s	1369 ms	8K

Inference speed

Gemma 2 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	58.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	26.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	26.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	26.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	20.9	Too big
RTX 3090 24GB	24 GB	Q4_K_M	17.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	16.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	12.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	11.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	10.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B58
Q3_K_S	3	13.2 GB	Low	B58
NVFP4	4	15.1 GB	Medium	B58
Q4_K_M	4	16.5 GB	Medium	B58
Q5_K_M	5	19.4 GB	High	B58
Q6_K	6	22.1 GB	High	B58
Q8_0	8	28.9 GB	Very High	B59
F16Best for your GPU	16	55.4 GB	Maximum	B63

Get started

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

Run

ollama run gemma2:27b

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Gemma 2 27B?

Yes, NVIDIA H200 PCIe 141GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 257.0 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 43.0 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 H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Gemma 2 27B achieves approximately 257.0 tokens per second decode speed with a time-to-first-token of 753ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA H200 PCIe 141GB receives a B grade with 257.0 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, 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 H200 PCIe 141GB See all hardware for Gemma 2 27B

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