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Can I run Gemma 2 2B (INT8 (8-bit Integer)) on NVIDIA A100 40GB?

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Perfect
Yes, you can run this model!
GPU VRAM
40.0GB
Required
2.0GB
Headroom
+38.0GB

VRAM Usage

0GB 5% used 40.0GB

Performance Estimate

Tokens/sec ~117.0
Batch size 32
Context 8192K

info Technical Analysis

NVIDIA A100 40GB provides excellent compatibility with Gemma 2 2B (2.00B). With 40.0GB of VRAM and only 2.0GB required, you have 38.0GB 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 A100 40GB 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 A100 40GB? expand_more
NVIDIA A100 40GB has 40.0GB VRAM, which provides 38.0GB 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 117 tokens per second.

GPU

memory
NVIDIA A100 40GB
40.0GB VRAM

AI Model

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

Alternative Quantizations

q3_k_m Perfect Q4_K_M (GGUF 4-bit) Perfect Perfect

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