COMPLETED
Research
Infer-GC
Garbage Collection-style memory management for LLM inference achieving 37-52% GPU memory reduction
// DESCRIPTION
Infer-GC applies garbage collection concepts from programming languages to optimize memory usage during LLM inference.
Core Concept
Traditional LLM Memory: ┌──────────────────────────────────────────────────┐ │ KV Cache grows unbounded → OOM │ │ [Token 1][Token 2][Token 3]...[Token N] 💥 │ └──────────────────────────────────────────────────┘ Infer-GC Memory: ┌──────────────────────────────────────────────────┐ │ GC identifies and reclaims unused memory │ │ [Active][Active][Freed][Active][Freed][Active] │ │ Reference counting + Memory pooling │ └──────────────────────────────────────────────────┘
GC Strategies
- Reference Counting: Track active token references
- Memory Pooling: Reuse freed memory blocks
- KV Cache Optimization: Compress old cache entries
- Lazy Deallocation: Batch memory reclamation
Results
| Model | Memory Reduction | Throughput Impact |
|---|---|---|
| LLaMA-7B | 37% | -2% |
| LLaMA-13B | 45% | -3% |
| Mistral-7B | 52% | -1% |
Key Innovation
Unlike traditional memory optimization that requires model modification, Infer-GC works as a runtime layer that can be applied to any transformer-based model.
// HIGHLIGHTS
- Novel application of GC concepts to ML
- 37-52% memory reduction demonstrated
- Minimal throughput impact (<3%)
- Drop-in replacement for existing inference