# Create an MoE layer with 8 experts
# Process a batch with variable expert utilization
# Measure memory savings vs standard implementation

num_experts = 8
expert_tokens = [64, 128, 96, 112, 88, 144, 72, 104]  # Realistic distribution
hidden_dim = 2048

# Your implementation here:
# 1. Set up grouped GEMM inputs
# 2. Convert to FP8 
# 3. Run DeepGEMM
# 4. Compare with standard PyTorch
# 5. Measure performance and memory usage

print("Challenge: Implement efficient MoE with DeepGEMM!")