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AI course essential tutorial

5D Parallelism
Memory components in LLM
P/D disaggregation
Continuous Batching
Decode-Maximal Batching
Chunked Prefill
Layerwise timing of LLM
LLM Inference
Lifecycle Aware Performance
System-Level Parallelism & Scaling [AI workload]
INT8 Quantization
[Calculate] Communication and compute overhead
[Calculate] LLM memory calculations
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INT8 Quantization

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5D Parallelism
Memory components in LLM
P/D disaggregation

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AI course essential

System-Level Parallelism & Scaling [AI workload]

When you have 100K GPUs, you want to utilize all of them.

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AI course essential

[Calculate] Communication and compute overhead

The embedding layer (before Attention layer) expands each token to a 1D vector of size hidden_dim. The common sizes of hidden dimension are 1024 to 8096.

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INT8 Quantization