Running infinite context lengths on 8GB GPU without ever hitting Out Of Memory (github.com)

Symphony, a groundbreaking constant-memory sequence modeling engine, has been announced, enabling Large Language Models (LLMs) like Qwen-7B to manage effectively infinite context lengths on an 8GB GPU without running out of memory. This is achieved through its innovative combination of Frequency Holographic Reduced Representations (FHRR) and a recurrent coordinate-based pointer network (HEP-DNA). Symphony maintains a consistent 5.49GB VRAM footprint while handling over 43,000 tokens, thus avoiding the problematic linear growth associated with traditional Key-Value (KV) caches. This advancement is significant for the AI/ML community as it allows for more efficient and powerful model training and inference, critical for applications requiring extensive contextual understanding. Notably, Symphony boasts 100% retrieval accuracy utilizing its Token Rarity Oracle and Holographic Exact Pointer for pinpoint precision in data retrieval, coupled with a minimal ~15% increase in perplexity during compression. Additionally, the active selective holographic-compression (Symphony ASH-C) technique compresses non-essential historical data into manageable matrices, providing a remarkable 4.6x speedup. Open-source and highly reproducible, Symphony represents a promising leap toward more adaptive and resource-efficient models in the competitive landscape of AI technologies.