Unverified50% confidenceFactExact time
Prompt Caching stores its cache in GPU memory, and unlimited caching will crowd out available inference memory, so a reasonable hot/cold eviction strategy is needed.
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10/12/2026
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Unverified研究《Accelerating Block Low-Rank Foundation Model Inference on Memory-Constrained GPUs》提出基于块低秩分解的方案压缩内存并加速推理77% similarVerifiedThe GPU memory hierarchy from fastest to slowest is: registers, L1 cache/shared memory, L2 cache, global memory (VRAM).76% similarVerifiedThe KV Cache (Key-Value Cache) strategy stores already-computed attention key-value pairs in GPU memory for reuse, which consumes a large amount of GPU memory resources.75% similarUnverifiedWhen model weights and KV Cache cannot be fully loaded into GPU VRAM and some data must be offloaded to main memory, inference speed drops by one to two orders of magnitude.73% similarUnverifiedGPU solutions use batching technology to combine multiple user requests for computation, which can greatly increase overall throughput and represents a trade-off between latency and throughput.72% similar
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