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Can I run Qwen 2.5 32B (Q4_K_M (GGUF 4-bit)) on NVIDIA A100 40GB?

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Perfect
Yes, you can run this model!
GPU VRAM
40.0GB
Required
16.0GB
Headroom
+24.0GB

VRAM Usage

0GB 40% used 40.0GB

Performance Estimate

Tokens/sec ~78.0
Batch size 3
Context 131072K

info Technical Analysis

NVIDIA A100 40GB provides excellent compatibility with Qwen 2.5 32B (32.00B). With 40.0GB of VRAM and only 16.0GB required, you have 24.0GB of headroom for comfortable inference. This allows for extended context lengths, batch processing, and smooth operation.

lightbulb Recommendation

You can run Qwen 2.5 32B (32.00B) on NVIDIA A100 40GB without any compromises. Consider using full context length and larger batch sizes for optimal throughput.

tune Recommended Settings

Batch_Size
3
Context_Length
131072
Inference_Framework
llama.cpp or vLLM

help Frequently Asked Questions

Can I run Qwen 2.5 32B (32.00B) on NVIDIA A100 40GB? expand_more
NVIDIA A100 40GB has 40.0GB VRAM, which provides 24.0GB of headroom beyond the 16.0GB required by Qwen 2.5 32B (32.00B). This is plenty of room for comfortable inference with room for KV cache, batching, and extended context lengths.
How much VRAM does Qwen 2.5 32B (32.00B) need? expand_more
Qwen 2.5 32B (32.00B) requires approximately 16.0GB of VRAM.
What performance can I expect? expand_more
Estimated 78 tokens per second.

GPU

memory
NVIDIA A100 40GB
40.0GB VRAM

AI Model

smart_toy
Qwen 2.5 32B (32.00B)
32.00B params

Alternative Quantizations

q3_k_m Perfect INT8 (8-bit Integer) Perfect

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