Tag: ComputeOptimization

FP8 Mixed-Precision Training

Posted on by lechuck park

FP8 Mixed-Precision Training Interpretation

This image is a technical diagram showing FP8 (8-bit Floating Point) Mixed-Precision Training methodology.

Three Main Architectures

1. Mixture of Experts (MoE)

  • Input: Starts with BF16 precision
  • Calc (1): Router output & input hidden states → BF16
  • Calc (2): Expert FFN (Feed-Forward Network) → FP8 computation
  • Calc (3): Accumulation → FP32
  • Transmit (Dispatch): Token dispatch (All-to-All) → FP8
  • Transmit (Combine): Combine expert outputs → BF16
  • Output: BF16

2. Multi-head Latent Attention

  • Input: BF16
  • Calc (1): Input hidden states → BF16
  • Calc (2): Projection/Query/Key/Value → FP8
  • Calc (3): Key/Value compression → BF16
  • Stabilization: RMSNorm → FP32
  • Output: Output hidden states → BF16

3. Multi-Token Prediction

  • Input: BF16
  • Calc (1): Embedding layer output → BF16
  • Calc (2): Transformer block → FP8
  • Calc (3): RMSNorm → FP32
  • Calc (4): Linear projection → BF16
  • Output: Output hidden states → BF16

Precision Strategy (Bottom Boxes)

🟦 BF16 (Default)

  • Works for most tasks
  • Balanced speed/stability

🟪 BF8 (Fastest)

  • For large compute/data movement
  • Very energy-efficient

🟣 BF32 (Safest/Most Precise)

  • For accuracy-critical or sensitive math operations

Summary

FP8 mixed-precision training strategically uses different numerical precisions across model operations: FP8 for compute-intensive operations (FFN, attention, transformers) to maximize speed and efficiency, FP32 for sensitive operations like accumulation and normalization to maintain numerical stability, and BF16 for input/output and communication to balance performance. This approach enables faster training with lower energy consumption while preserving model accuracy, making it ideal for training large-scale AI models efficiently.

#FP8Training #MixedPrecision #AIOptimization #DeepLearning #ModelEfficiency #NeuralNetworks #ComputeOptimization #MLPerformance #TransformerTraining #EfficientAI #LowPrecisionTraining #AIInfrastructure #MachineLearning #GPUOptimization #ModelTraining

With Claude

LLM goes with Computing-Power-Cooling

Posted on by lechuck park

LLM’s Computing-Power-Cooling Relationship

This diagram illustrates the technical architecture and potential issues that can occur when operating LLMs (Large Language Models).

Normal Operation (Top Left)

  1. Computing Requires – LLM workload is delivered to the processor
  2. Power Requires – Power supplied via DVFS (Dynamic Voltage and Frequency Scaling)
  3. Heat Generated – Heat is produced during computing processes
  4. Cooling Requires – Temperature management through proper cooling systems

Problem Scenarios

Power Issue (Top Right)

  • Symptom: Insufficient power (kW & Quality)
  • Results:
    • Computing performance degradation
    • Power throttling or errors
    • LLM workload errors

Cooling Issue (Bottom Right)

  • Symptom: Insufficient cooling (Temperature & Density)
  • Results:
    • Abnormal heat generation
    • Thermal throttling or errors
    • Computing performance degradation
    • LLM workload errors

Key Message

For stable LLM operations, the three elements of Computing-Power-Cooling must be balanced. If any one element is insufficient, it leads to system-wide performance degradation or errors. This emphasizes that AI infrastructure design must consider not only computing power but also adequate power supply and cooling systems together.

Summary

  • LLM operation requires a critical balance between computing, power supply, and cooling infrastructure.
  • Insufficient power causes power throttling, while inadequate cooling leads to thermal throttling, both resulting in workload errors.
  • Successful AI infrastructure design must holistically address all three components rather than focusing solely on computational capacity.

#LLM #AIInfrastructure #DataCenter #ThermalManagement #PowerManagement #AIOperations #MachineLearning #HPC #DataCenterCooling #AIHardware #ComputeOptimization #MLOps #TechInfrastructure #AIatScale #GreenAI

WIth Claude