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URL: https://willitrunai.com/can-run/gemma-2-27b-on-rtx-pro-4500-blackwell-32gb


Can Gemma 2 27B run on RTX PRO 4500 Blackwell 32GB?

YES — With Offload

A70Great
Estimated from fit model

Gemma 2 27B needs ~32.1 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 36.5 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

36.5 tok/s

TTFT

5310 ms

Safe context

8K

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 PRO 4500 Blackwell 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: 36.5 tok/s decode · 5.3s TTFT (warm) · 91 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

WorkloadGradeFitDecodeTTFTContext
ChatATight fit48.0 tok/s2201 ms8K
CodingARuns with offload (needs ~0.1 GB host RAM)36.5 tok/s5310 ms8K
Agentic CodingFToo heavy19.8 tok/s14244 ms8K
ReasoningARuns with offload (needs ~0.1 GB host RAM)36.5 tok/s6276 ms8K
RAGFToo heavy18.8 tok/s18695 ms

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on RTX PRO 4500 Blackwell 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
10.5 GB
LowB66
Q3_K_S
3
13.2 GB
LowB67
NVFP4
4

Get started

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

Run

ollama run gemma2:27b

Your hardware

More models your RTX PRO 4500 Blackwell 32GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
30.5BS113.8 tok/s
👁 Alibaba
Qwen 3.6 35B A3B
35BS95.6 tok/s

Frequently asked questions

See all results for RTX PRO 4500 Blackwell 32GBSee all hardware for Gemma 2 27B
8K
15.1 GB
Medium
B68
Q4_K_M
4
16.5 GB
MediumB69
Q5_K_M
5
19.4 GB
HighB69
Q6_KBest for your GPU
6
22.1 GB
HighB68
Q8_0
8
28.9 GB
Very HighF0
F16
16
55.4 GB
MaximumF0
👁 Alibaba
Qwen3-VL 30B A3B Instruct
30BS117.7 tok/s
👁 Alibaba
Qwen 3.5 35B A3B
35BS104 tok/s
👁 Alibaba
Qwen 3 32B
32BS41.9 tok/s

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