KV optimizations & inference advances (LookaheadKV, TurboQuant, DeepSeek-V4, HISA etc.)
Key Questions
What KV cache optimization does DeepSeek-V4 use?
DeepSeek-V4 employs hybrid CSA/HCA attention, reducing KV cache to 10% for 1M context length. This ties into Newton-Schulz insights for efficiency.
What is TurboQuant and its benefits?
TurboQuant achieves 6x compression at 3-bit quantization for KV caches. It helps alleviate memory bottlenecks in LLM inference.
How does KVTC improve inference?
KVTC provides 20x KV cache compression. It enables faster processing for long-context scenarios.
What speedups does vLLM LMCache offer?
vLLM LMCache delivers 4-15x inference speedups. It optimizes caching for repeated prompts.
What is HISA and its performance?
HISA offers 3.75x speedup in inference. It focuses on efficient attention for long contexts, comparable to Gemma4/GLM.
What is ForesightKV?
ForesightKV is an eviction policy for KV caches. It predicts and removes less important tokens to save memory.
How does TRL OPD impact performance?
TRL OPD boosts AIME scores by +39 points. It uses on-policy distillation for better reasoning.
What savings come from speculative decoding?
Speculative decoding saves 19% in compute. It predicts tokens to accelerate generation.
DeepSeek-V4 CSA/HCA (10% KV/1M ctx), TurboQuant 6x/3-bit, KVTC 20x, vLLM LMCache 4-15x, HISA 3.75x, Gemma4/GLM perf, ForesightKV eviction, TRL OPD +39 AIME, spec decode 19% savings. Ties DeepSeek-V4 Newton-Schulz insights. Next: DeepSeek/Gemma4 repros/overhead/Helium benches.