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Can I run Gemma 2 2B (INT8 (8-bit Integer)) on NVIDIA RTX 3090 Ti?

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Perfect

Yes, you can run this model!

GPU VRAM

24.0GB

Required

2.0GB

Headroom

+22.0GB

VRAM Usage

0GB 8% used 24.0GB

Performance Estimate

Tokens/sec ~90.0

Batch size 32

Context 8192K

info Technical Analysis

NVIDIA RTX 3090 Ti provides excellent compatibility with Gemma 2 2B (2.00B). With 24.0GB of VRAM and only 2.0GB required, you have 22.0GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Gemma 2 2B (2.00B) on NVIDIA RTX 3090 Ti without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size

Context_Length

8192

Inference_Framework

llama.cpp or vLLM

help Frequently Asked Questions

Can I run Gemma 2 2B (2.00B) on NVIDIA RTX 3090 Ti? expand_more

NVIDIA RTX 3090 Ti has 24.0GB VRAM, which provides 22.0GB of headroom beyond the 2.0GB required by Gemma 2 2B (2.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.

How much VRAM does Gemma 2 2B (2.00B) need? expand_more

Gemma 2 2B (2.00B) requires approximately 2.0GB of VRAM.

What performance can I expect? expand_more

Estimated 90 tokens per second.