Can Gemma 2 27B run on RTX 5090 32GB?

YES — With Offload

A72Great

Estimated from fit model

Gemma 2 27B needs ~32.1 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 32.1 GB, 58.2 tok/s, Runs with offload (needs ~0.1 GB host RAM)

32.1 GB required32.0 GB available

100% VRAM needed

0.1 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.1 GB host RAM)

Decode

58.2 tok/s

TTFT

3329 ms

Safe context

Memory

32.1 GB / 32.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 2 27B on RTX 5090 32GB
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: 58.2 tok/s decode · 3.3s TTFT (warm) · 145 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	76.5 tok/s	1380 ms	8K
Coding	A	Runs with offload (needs ~0.1 GB host RAM)	58.2 tok/s	3329 ms	8K
Agentic Coding	F	Too heavy	31.5 tok/s	8929 ms	8K
Reasoning	A	Runs with offload (needs ~0.1 GB host RAM)	58.2 tok/s	3934 ms	8K
RAG	F	Too heavy	31.5 tok/s	11161 ms	8K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B66
Q3_K_S	3	13.2 GB	Low	B67
NVFP4	4	15.1 GB	Medium	B68
Q4_K_M	4	16.5 GB	Medium	B69
Q5_K_M	5	19.4 GB	High	B69
Q6_KBest for your GPU	6	22.1 GB	High	B68
Q8_0	8	28.9 GB	Very High	F0
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 RTX 5090 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	181.6 tok/s
Qwen 3.6 35B A3B	35B	S	128.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	187.8 tok/s
Qwen 3.5 35B A3B	35B	S	139.4 tok/s
Qwen 3 32B	32B	S	66.9 tok/s

Frequently asked questions

Can RTX 5090 32GB run Gemma 2 27B?

Yes, RTX 5090 32GB can run Gemma 2 27B with a A grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 58.2 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 32.1 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 RTX 5090 32GB?

On RTX 5090 32GB, Gemma 2 27B achieves approximately 58.2 tokens per second decode speed with a time-to-first-token of 3329ms using Q4_K_M quantization.

Can RTX 5090 32GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on RTX 5090 32GB receives a A grade with 58.2 tok/s and 8K context.

What context window can Gemma 2 27B use on RTX 5090 32GB?

On RTX 5090 32GB, 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.

What should I upgrade first if Gemma 2 27B feels slow on RTX 5090 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 5090 32GB See all hardware for Gemma 2 27B

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