Due to the need for long-sequence support in large language models (LLMs), a solution to the problematic key-value (KV) cache bottleneck needs addressing. LLMs like GPT-4, Gemini, and LWM are becoming increasingly prominent in apps such as chatbots and financial analysis, but the substantial memory footprint of the KV cache and their auto-regressive nature make these models difficult to handle.
Previous tactics suggested using KV cache eviction strategies to cut down on the impact on memory, but these often resulted in data loss ultimately causing comprehension issues within longer contexts. Speculative decoding has been proposed as a solution, however, this requires comprehensive calculation to train draft models.
Researchers from Carnegie Mellon University and Meta AI have put forth TriForce, a system of hierarchical speculative decoding designed for long sequence generation scalability. This technique relies on the original model weights and dynamic KV cache to delineate an intermediate layer within the model. This method allows the full cache to remain intact, which provides superior KV cache selections that are rated as lossless.
TriForce has been shown to achieve impressive speed increases, as well as improved efficiencies, particularly when offloading to consumer-grade graphics cards. In conclusion, the addition of TriForce to the hierarchy of decoding systems provides extreme speed boosts and clear potential for revolutionizing long-context model deployments.
