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Running large language models efficiently is more than just raw GPU power. The latest guide breaks down the essential math to determine if your LLM workload is compute-bound or memory-bound.
We apply these principles to a real-world example: Qwen's 32B parameter model on the new NVIDIA RTX PRO 6000 Blackwell Edition.
In this guide, you will learn how to:
Calculate your GPU's operational intensity (Ops:Byte Ratio)
Determine your model's arithmetic intensity
Identify whether your workload is memory-bound or compute-bound
Read the full guide here: https://flozi.net/en/guides/ai/llm-inference-math
We apply these principles to a real-world example: Qwen's 32B parameter model on the new NVIDIA RTX PRO 6000 Blackwell Edition.
In this guide, you will learn how to:
Calculate your GPU's operational intensity (Ops:Byte Ratio)
Determine your model's arithmetic intensity
Identify whether your workload is memory-bound or compute-bound
Read the full guide here: https://flozi.net/en/guides/ai/llm-inference-math