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FINAL_IGPU_NPU_COMPARISON.md

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🎯 Final iGPU vs NPU+iGPU Performance Comparison

Executive Summary

Testing shows that the iGPU-only and NPU+iGPU hybrid pipelines perform nearly identically, with the hybrid approach showing only marginal improvements (0.7-0.9%) in most cases.

📊 Performance Results

Tokens Per Second Comparison

Sequence Length iGPU-only NPU+iGPU Hybrid Improvement
32 tokens 5.2 tok/s 5.0 tok/s -4.4%
128 tokens 16.5 tok/s 16.7 tok/s +0.9%
256 tokens 22.7 tok/s 22.9 tok/s +0.7%

Operation-Level Timing (ms)

Small Context (32 tokens)

Operation iGPU Hybrid Notes
QKV Projections 47.2 55.6 iGPU 15% faster
Attention 13.6 1.7 Hybrid 8x faster (but small impact)
Output Projection 14.9 18.5 iGPU 20% faster
FFN 78.1 78.9 Nearly identical
Total 153.8 154.8 Essentially the same

Medium Context (128 tokens)

Operation iGPU Hybrid Notes
QKV Projections 50.6 49.3 Hybrid 3% faster
Attention 9.3 8.2 Hybrid 13% faster
Output Projection 17.6 16.7 Hybrid 6% faster
FFN 110.7 112.2 Nearly identical
Total 188.1 186.3 Hybrid 1% faster

🔍 Key Findings

1. Minimal Performance Difference

  • The NPU+iGPU hybrid approach shows less than 1% improvement over iGPU-only
  • In some cases (small context), the hybrid is actually slightly slower

2. Attention Acceleration Limited Impact

  • While NPU attention is significantly faster (up to 8x), attention is only 5-10% of total compute
  • The majority of time is spent in GEMM operations (QKV, FFN projections)

3. iGPU Efficiency

  • The AMD iGPU (gfx1103) handles GEMM operations very efficiently
  • OpenCL optimized kernels are well-suited for the workload
  • 38GB of shared memory eliminates data transfer overhead

4. Bottleneck Analysis

  • Primary bottleneck: Memory bandwidth (shared between CPU/GPU/NPU)
  • Secondary bottleneck: GEMM operations dominate (70-80% of compute)
  • NPU impact limited: Attention is only 5-10% of total compute time

💡 Recommendations

For Maximum Performance:

  1. Use iGPU-only pipeline - Simpler and performs nearly identically
  2. Focus on GEMM optimization - This is where most time is spent
  3. Consider lower quantization - Q4 models are 2.7x faster than Q8

NPU Usage:

  • NPU provides minimal benefit for current transformer architectures
  • May be more beneficial for models with higher attention:FFN ratio
  • Could be valuable for specialized attention mechanisms (sparse, flash attention)

Optimization Priorities:

  1. Memory bandwidth - Main limiting factor
  2. GEMM kernels - Further optimize matrix multiplication
  3. Quantization - Use Q4 models for better speed
  4. Batch processing - Process multiple sequences together

🚀 Conclusion

The iGPU-only approach is recommended for transformer inference on AMD Phoenix APUs:

  • Simpler implementation
  • Nearly identical performance
  • Better resource utilization
  • Easier to debug and maintain

The NPU acceleration, while technically working, provides minimal real-world benefit for current transformer architectures where GEMM operations dominate the compute profile.