Can Gemma 2 27B run on NVIDIA A16 64GB?

YES — Runs Great

B70Good

Estimated from fit model

Gemma 2 27B needs ~35.3 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 35.3 GB, 29.8 tok/s, Runs well

35.3 GB required64.0 GB available

55% VRAM used

Fit status

Runs well

Decode

29.8 tok/s

TTFT

6489 ms

Safe context

Memory

35.3 GB / 64.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsGemma 2 27B on NVIDIA A16 64GB
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: 29.8 tok/s decode · 6.5s TTFT (warm) · 75 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	29.8 tok/s	3539 ms	8K
Coding	B	Runs well	29.8 tok/s	6489 ms	8K
Agentic Coding	A	Runs well	29.8 tok/s	9438 ms	8K
Reasoning	B	Runs well	29.8 tok/s	7669 ms	8K
RAG	A	Runs well	29.8 tok/s	11798 ms	8K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B61
Q3_K_S	3	13.2 GB	Low	B62
NVFP4	4	15.1 GB	Medium	B62
Q4_K_M	4	16.5 GB	Medium	B62
Q5_K_M	5	19.4 GB	High	B63
Q6_K	6	22.1 GB	High	B64
Q8_0Best for your GPU	8	28.9 GB	Very High	B65
F16	16	55.4 GB	Maximum	F0

Get started

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

Run

ollama run gemma2:27b

升级选项

能流畅运行 Gemma 2 27B 的硬件

RTX PRO 6000 Blackwell Workstation Edition 96GB经济之选

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.96 tok/s 解码

Raises estimated decode speed by about 222%.

Adds memory headroom for longer context windows and future model growth.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GB最佳性价比

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 187%.85.5 tok/s 解码

Raises estimated decode speed by about 187%.

Adds memory headroom for longer context windows and future model growth.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA 升级

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 593%.206.6 tok/s 解码

Raises estimated decode speed by about 593%.

Adds memory headroom for longer context windows and future model growth.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Gemma 2 27B?

Yes, NVIDIA A16 64GB can run Gemma 2 27B with a B grade (Runs well). Expected decode speed: 29.8 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 35.3 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 A16 64GB?

On NVIDIA A16 64GB, Gemma 2 27B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6489ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA A16 64GB receives a B grade with 29.8 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 A16 64GB See all hardware for Gemma 2 27B

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