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Can I run Gemma 2 9B (Q4_K_M (GGUF 4-bit)) on NVIDIA RTX 4090?

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Perfect
Yes, you can run this model!
GPU VRAM
24.0GB
Required
4.5GB
Headroom
+19.5GB

VRAM Usage

0GB 19% used 24.0GB

Performance Estimate

Tokens/sec ~72.0
Batch size 10
Context 8192K

info Technical Analysis

NVIDIA RTX 4090 provides excellent compatibility with Gemma 2 9B (9.00B). With 24.0GB of VRAM and only 4.5GB required, you have 19.5GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Gemma 2 9B (9.00B) on NVIDIA RTX 4090 without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size
10
Context_Length
8192
Inference_Framework
llama.cpp or vLLM

help Frequently Asked Questions

Can I run Gemma 2 9B (9.00B) on NVIDIA RTX 4090? expand_more
NVIDIA RTX 4090 has 24.0GB VRAM, which provides 19.5GB of headroom beyond the 4.5GB required by Gemma 2 9B (9.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.
How much VRAM does Gemma 2 9B (9.00B) need? expand_more
Gemma 2 9B (9.00B) requires approximately 4.5GB of VRAM.
What performance can I expect? expand_more
Estimated 72 tokens per second.

GPU

memory
NVIDIA RTX 4090
24.0GB VRAM

AI Model

smart_toy
Gemma 2 9B (9.00B)
9.00B params

Alternative Quantizations

q3_k_m Perfect INT8 (8-bit Integer) Perfect Perfect

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