Tag: AIEngineering

Basic LLM Workflow

Posted on by lechuck park

Basic LLM Workflow Interpretation

This diagram illustrates how data flows through various hardware components during the inference process of a Large Language Model (LLM).

Step-by-Step Breakdown

① Initialization Phase (Warm weights)

  • Model weights are loaded from SSD → DRAM → HBM (High Bandwidth Memory)
  • Weights are distributed and shared across multiple GPUs

② Input Processing (CPU tokenizes/batches)

  • CPU tokenizes input text and processes batches
  • Data is transferred through DRAM buffer to GPU

③ GPU Inference Execution

  • GPU performs Attention and FFN (Feed-Forward Network) computations from HBM
  • KV cache (Key-Value cache) is stored in HBM
  • If HBM is tight, KV cache can be offloaded to DRAM or SSD

④ Distributed Communication (NvLink/Infiniband)

  • Intra-node: High-speed communication between GPUs via NvLink (with NVSwitch if available)
  • Inter-node: Parallel communication through InfiniBand or NCCL

⑤ Post-processing (CPU decoding/post)

  • CPU decodes generated tokens and performs post-processing
  • Logs and caches are saved to SSD

Key Characteristics

This architecture leverages a memory hierarchy to efficiently execute large-scale models:

  • SSD: Long-term storage (slowest, largest capacity)
  • DRAM: Intermediate buffer
  • HBM: GPU-dedicated high-speed memory (fastest, limited capacity)

When model size exceeds GPU memory, strategies include distributing across multiple GPUs or offloading data to higher-level memory tiers.

Summary

This diagram shows how LLMs process data through a memory hierarchy (SSD→DRAM→HBM) across CPU and GPU components. The workflow involves loading model weights, tokenizing inputs on CPU, running inference on GPU with HBM, and using distributed communication (NvLink/InfiniBand) for multi-GPU setups. Memory management strategies like KV cache offloading enable efficient execution of large models that exceed single GPU capacity.

#LLM #DeepLearning #GPUComputing #MachineLearning #AIInfrastructure #NeuralNetworks #DistributedComputing #HPC #ModelOptimization #AIArchitecture #NvLink #Transformer #MLOps #AIEngineering #ComputerArchitecture

With Claude