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Can I run Qwen2-VL 7B on NVIDIA RTX 5000 Ada?

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Perfect
Yes, you can run this model!
GPU VRAM
32.0GB
Required
14.0GB
Headroom
+18.0GB

VRAM Usage

0GB 44% used 32.0GB

Performance Estimate

Tokens/sec ~90.0
Batch size 12

info Technical Analysis

NVIDIA RTX 5000 Ada provides excellent compatibility with Qwen2-VL 7B. With 32.0GB of VRAM and only 14.0GB required, you have 18.0GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Qwen2-VL 7B on NVIDIA RTX 5000 Ada without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size
12
Context_Length
32768
Inference_Framework
llama.cpp or vLLM

help Frequently Asked Questions

Can I run Qwen2-VL 7B on NVIDIA RTX 5000 Ada? expand_more
NVIDIA RTX 5000 Ada has 32.0GB VRAM, which provides 18.0GB of headroom beyond the 14.0GB required by Qwen2-VL 7B (7.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.
How much VRAM does Qwen2-VL 7B need? expand_more
Qwen2-VL 7B requires approximately 14.0GB of VRAM.
What performance can I expect? expand_more
Estimated 90 tokens per second.

GPU

memory
NVIDIA RTX 5000 Ada
32.0GB VRAM

AI Model

smart_toy
Qwen2-VL 7B (7.00B)
7.00B params

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