LLM architecture advances: KV sharing/mHC + SP-KV + MTP + EAGLE 3.1 + JetSpec + dMoE + TurboQuant + Value-Aware KV eviction + FlashMemory LSA + REAP + Block-GTQ + FlashMorph for long-context and MoE efficiency
Key Questions
How do recent KV cache optimizations impact VRAM usage for long contexts?
KV optimizations can reduce VRAM requirements enough to support 1M token contexts within 32-64GB of memory. Techniques like TurboQuant provide 5x compression, Value-Aware Eviction achieves 4x compression, and FlashMemory reduces the KV cache to 13.5% of its original size.
What is FlashMorph and how does it improve long-context efficiency?
FlashMorph converts selected attention layers to linear attention within hybrid models. This approach retains only a subset of full-attention layers while improving efficiency for long-context inference.
Which speculative decoding methods are highlighted for faster inference?
EAGLE 3.1 is integrated in vLLM, JetSpec delivers up to 9.64x speedup, and MTP is available in llama.cpp and LM Studio. These methods accelerate token generation without changing model quality.
What benefits does dMoE provide for Mixture-of-Experts models?
dMoE uses block-level routing to reduce the number of activated experts by approximately 80%. This lowers compute and memory costs during inference while maintaining model performance.
Why is paged attention important for LLM inference?
Paged attention addresses the 60-80% memory wastage commonly seen in standard KV cache management. It enables more efficient GPU memory usage and supports higher throughput during inference.
KV optimizations reduce VRAM for 1M ctx in 32-64GB; TurboQuant open-source 5x KV cache compression; Value-Aware Eviction achieves 4x compression; FlashMemory reduces KV cache to 13.5%; Block-GTQ achieves 3.24x compression. FlashMorph proposes converting attention layers to linear attention in hybrid models for long-context efficiency. Speculative decoding: EAGLE 3.1 in vLLM, JetSpec up to 9.64x speedup, MTP in llama.cpp/LM Studio. dMoE block-level routing reduces activated experts ~80%. A recent explainer on KV cache and paged attention highlighted 60-80% memory wastage, reinforcing the importance of paged attention for efficient inference. A new survey on efficient attention mechanisms further underscores the practical importance of these techniques for local deployment.