ModelEfficiency – 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.

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With Claude