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Can I run Gemma 2 2B (q3_k_m) on NVIDIA RTX 3090 Ti?

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Perfect
Yes, you can run this model!
GPU VRAM
24.0GB
Required
0.8GB
Headroom
+23.2GB

VRAM Usage

0GB 3% 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 0.8GB required, you have 23.2GB 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
32
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 23.2GB of headroom beyond the 0.8GB 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 0.8GB of VRAM.
What performance can I expect? expand_more
Estimated 90 tokens per second.

GPU

memory
NVIDIA RTX 3090 Ti
24.0GB VRAM

AI Model

smart_toy
Gemma 2 2B (2.00B)
2.00B params

Alternative Quantizations

Q4_K_M (GGUF 4-bit) Perfect INT8 (8-bit Integer) Perfect Perfect

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