Inference-Time Hyper-Scaling with KV Cache Compression
Inference-time scaling trades efficiency for increased reasoning accuracy by
generating longer or more parallel sequences. However, in Transformer LLMs,
generation cost is bottlenecked by the size of the key-value (KV) cache, rather
than the number of generated tokens. Hence, we explore inference-time
hyper-scaling: by compressing the KV cache, we can generate more tokens within
the same compute budget and further improve the accuracy of scaled inference.
The success of this approach, however, hinges on the ability of compression
methods to preserve accuracy even at high compression ratios. To make
hyper-scaling practical, we introduce Dynamic Memory Sparsification (DMS), a
novel method for sparsifying KV caches that only requires 1K training steps to
achieve 8times compression, while maintaining better accuracy than
training-free sparse attention. Instead of prematurely discarding cached
tokens, DMS delays token eviction, implicitly merging representations and
preserving critical information. We demonstrate the effectiveness of
inference-time hyper-scaling with DMS on multiple families of LLMs, showing
that it boosts accuracy for comparable inference runtime and memory load. For
instance, we enhance Qwen-R1 32B by an average of 9.1 points on AIME 24, 7.6 on
GPQA, and 9.6 on LiveCodeBench across compute budgets.
6842a3cb9393cafb521855aa
Adrian Łańcucki
6842a3cb9393cafb521855ab
Konrad Staniszewski
6842a3cb9393cafb521855ac
Piotr Nawrot
6842a3cb9393cafb521855ad
Edoardo M. Ponti
claimed_verified
2025-06-10T11:00:21.732Z
60809ad44ad99100d63ce36a
https://cdn-avatars.huggingface.co/v1/production/uploads/1619040921084-noauth.jpeg
Edoardo Maria Ponti
user
ducdauge
Inference-Time Hyper-Scaling with KV Cache Compression
Inference-time scaling trades efficiency for increased reasoning accuracy by
generating longer or more parallel sequences. However, in Transformer LLMs,
generation cost is bottlenecked by the size of the key-value (KV) cache, rather
than the number of generated tokens. Hence, we explore inference-time
hyper-scaling: by compressing the KV cache, we can generate more tokens within
the same compute budget and further improve the accuracy of scaled inference.
The success of this approach, however, hinges on the ability of compression
methods to preserve accuracy even at high compression ratios. To make
hyper-scaling practical, we introduce Dynamic Memory Sparsification (DMS), a
novel method for sparsifying KV caches that only requires 1K training steps to
achieve 8times compression, while maintaining better accuracy than
training-free sparse attention. Instead of prematurely discarding cached
tokens, DMS delays token eviction, implicitly merging representations and
preserving critical information. We demonstrate the effectiveness of
inference-time hyper-scaling with DMS on multiple families of LLMs, showing
that it boosts accuracy for comparable inference runtime and memory load. For
instance, we enhance Qwen-R1 32B by an average of 9.1 points on AIME 24, 7.6 on
GPQA, and 9.6 on LiveCodeBench across compute budgets.
Inference-time hyper-scaling with Dynamic Memory Sparsification in Transformer LLMs allows for increased token generation within the same compute budget by compressing the key-value cache, thereby enhancing accuracy.
inference-time hyper-scaling key-value (KV) cache Dynamic Memory Sparsification (DMS) token eviction representation merging AIME 24 GPQA LiveCodeBench Qwen-R1 32B