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Can I run Gemma 2 27B (Q4_K_M (GGUF 4-bit)) on NVIDIA A100 80GB?

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Perfect
Yes, you can run this model!
GPU VRAM
80.0GB
Required
13.5GB
Headroom
+66.5GB

VRAM Usage

0GB 17% used 80.0GB

Performance Estimate

Tokens/sec ~78.0
Batch size 12
Context 8192K

info Technical Analysis

NVIDIA A100 80GB provides excellent compatibility with Gemma 2 27B (27.00B). With 80.0GB of VRAM and only 13.5GB required, you have 66.5GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Gemma 2 27B (27.00B) on NVIDIA A100 80GB without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size
12
Context_Length
8192
Inference_Framework
llama.cpp or vLLM

help Frequently Asked Questions

Can I run Gemma 2 27B (27.00B) on NVIDIA A100 80GB? expand_more
NVIDIA A100 80GB has 80.0GB VRAM, which provides 66.5GB of headroom beyond the 13.5GB required by Gemma 2 27B (27.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.
How much VRAM does Gemma 2 27B (27.00B) need? expand_more
Gemma 2 27B (27.00B) requires approximately 13.5GB of VRAM.
What performance can I expect? expand_more
Estimated 78 tokens per second.

GPU

memory
NVIDIA A100 80GB
80.0GB VRAM

AI Model

smart_toy
Gemma 2 27B (27.00B)
27.00B params

Alternative Quantizations

q3_k_m Perfect INT8 (8-bit Integer) Perfect Perfect

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