1. Overcoming the LLM Memory Bandwidth Wall
LLM token generation is memory-bandwidth bound rather than compute-bound. Loading 70B parameter weights for every single token output underutilizes Tensor Cores. With Speculative Decoding and Medusa Heads, a lightweight 1B draft model predicts candidate tokens in parallel, verified in a single forward pass by the 70B target model.
Speculative Execution Directives
- Zero Accuracy Loss: Speculative sampling mathematically guarantees identical output probability distributions
- Parallel Verification: Target model validates N candidate tokens in a single parallel matrix multiplication pass
- Medusa Heads Integration: Eliminates separate draft models by training multi-token prediction heads directly on top of base model weights
2. Speculative Decoding Latency Benchmarks
| Inference Technique | Llama-3 70B Speed (Tokens/sec) | P95 Latency | VRAM Footprint |
|---|---|---|---|
| Standard Autoregressive Generation | 18.2 Tok/s | 55 ms/tok | 140 GB |
| Speculative Decoding (1B Draft Model) | 42.5 Tok/s | 23 ms/tok | 144 GB |
| InexpensiveCoders Medusa Multi-Head Engine | 51.0 Tok/s | 19 ms/tok | 142 GB |
