Can Gemma 2 27B run on NVIDIA A100 40GB?

YES — Runs Great

A75Great

Estimated from fit model

Gemma 2 27B needs ~32.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 32.6 GB, 51.7 tok/s, Runs well

32.6 GB required40.0 GB available

82% VRAM used

Fit status

Runs well

Decode

51.7 tok/s

TTFT

3741 ms

Safe context

Memory

32.6 GB / 40.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsGemma 2 27B on NVIDIA A100 40GB
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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 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	A	Runs well	51.7 tok/s	2041 ms	8K
Coding	A	Runs well	51.7 tok/s	3741 ms	8K
Agentic Coding	B	Very compromised (needs ~1.4 GB host RAM)	32.0 tok/s	8796 ms	8K
Reasoning	A	Runs well	51.7 tok/s	4421 ms	8K
RAG	B	Very compromised (needs ~1.4 GB host RAM)	32.0 tok/s	10995 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 A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B64
Q3_K_S	3	13.2 GB	Low	B65
NVFP4	4	15.1 GB	Medium	B66
Q4_K_M	4	16.5 GB	Medium	B66
Q5_K_M	5	19.4 GB	High	B68
Q6_K	6	22.1 GB	High	B68
Q8_0Best for your GPU	8	28.9 GB	Very High	B68
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

Your hardware

More models your NVIDIA A100 40GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	197.5 tok/s
Qwen 3.6 35B A3B	35B	S	166 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	204.3 tok/s
Qwen 3.5 35B A3B	35B	S	180.5 tok/s
Qwen 3 32B	32B	S	72.8 tok/s

Frequently asked questions

Can NVIDIA A100 40GB run Gemma 2 27B?

Yes, NVIDIA A100 40GB can run Gemma 2 27B with a A grade (Runs well). Expected decode speed: 51.7 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 32.6 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 A100 40GB?

On NVIDIA A100 40GB, Gemma 2 27B achieves approximately 51.7 tokens per second decode speed with a time-to-first-token of 3741ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on NVIDIA A100 40GB receives a A grade with 51.7 tok/s and 8K context.

What context window can Gemma 2 27B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, 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 A100 40GB See all hardware for Gemma 2 27B

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