Can Gemma 2 27B run on Quadro RTX 6000 24GB?

Q: What should I upgrade first if Gemma 2 27B feels slow on Quadro RTX 6000 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

B58Good

Estimated from fit model

Gemma 2 27B needs ~28.1 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q3_K_S quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Gemma 2 27B at Q4_K_M needs 31.3 GB — too much for Quadro RTX 6000 24GB (24.0 GB). Runs at Q3_K_S (28.1 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.3 GB, exceeds 24.0 GB available

31.3 GB required24.0 GB available

130% VRAM needed

7.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.0 tok/s

TTFT

16088 ms

Safe context

Memory

31.3 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 2 27B on Quadro RTX 6000 24GB
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: 12.0 tok/s decode · 16.1s TTFT (warm) · 30 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload	17.7 tok/s	5969 ms	6K
Coding	F	Too heavy	11.5 tok/s	16892 ms	6K
Agentic Coding	F	Too heavy	5.8 tok/s	48176 ms	6K
Reasoning	F	Too heavy	11.5 tok/s	19964 ms	6K
RAG	F	Too heavy	5.8 tok/s	60220 ms	6K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	B69
Q3_K_S	3	13.2 GB	Low	B70
NVFP4	4

Get started

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

Run

ollama run gemma2:27b

Upgrade options

Hardware that runs Gemma 2 27B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1120)

Makes the model fit on the accelerator instead of staying completely out of reach.58.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.36.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.21.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$4,000 MSRP

Frequently asked questions

Can Quadro RTX 6000 24GB run Gemma 2 27B?

Yes, Quadro RTX 6000 24GB can run Gemma 2 27B at Q3_K_S quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 31.3 GB which exceeds available memory, but at Q3_K_S it needs only 28.1 GB. Expected decode speed: 17.7 tok/s.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 31.3 GB at Q4_K_M quantization. On Quadro RTX 6000 24GB, it fits at Q3_K_S using 28.1 GB.

What is the best quantization for Gemma 2 27B?

The recommended quantization is Q4_K_M, but on Quadro RTX 6000 24GB the best fitting quantization is Q3_K_S, which uses 28.1 GB.

What speed will Gemma 2 27B run at on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Gemma 2 27B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10931ms using Q3_K_S quantization.

Can Quadro RTX 6000 24GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on Quadro RTX 6000 24GB receives a F grade with 11.5 tok/s and 6K context.

What context window can Gemma 2 27B use on Quadro RTX 6000 24GB?

On Quadro RTX 6000 24GB, Gemma 2 27B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for Quadro RTX 6000 24GB See all hardware for Gemma 2 27B

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