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  • arXiv:2504. 00970v1 [cs. CL] 1 Apr 2025
    Due to the transformer-based architecture of LLMs [26], the memory footprint of KV cache grows linearly with the context length, directly impacting GPU usage For example, in the case of the LLaMA-3 1-8B model [1], as shown in Appendix A 1, processing a 32k-token prompt during the decoding stage requires approximately 16 GB (using float16 precision) of GPU memory, which can be prohibitive for
  • What is KV Cache in LLMs and How Does It Help?
    The KV cache stores the computed key and value tensors for all previously generated tokens When generating a new token, only its key and value are computed and appended, while the model attends to the full cache
  • What is the KV cache? | Matt Log - GitHub Pages
    This approach leads to what is called the KV cache Note that the KV cache of one token depends on all its previous tokens, hence if we have the same token appearing in two different positions inside the sequence, the corresponding KV caches will be different as well How much memory does KV cache use? Let’s consider a 13B parameter OPT model
  • Understanding and Coding the KV Cache in LLMs from Scratch
    The next section illustrates this with a concrete code example Implementing a KV Cache from Scratch There are many ways to implement a KV cache, with the main idea being that we only compute the key and value tensors for the newly generated tokens in each generation step I opted for a simple one that emphasizes code readability
  • KV Cache 101: How Large Language Models Remember and Reuse Information
    What Is a KV Cache? At its core, a KV Cache (Key-Value Cache) is a memory-saving technique used in transformer-based models During inference, it stores intermediate representations— keys and values —generated at each layer for already-processed tokens When the model receives new input, it doesn’t start over
  • KV Caching Explained: Optimizing Transformer Inference Efficiency
    KV caching is a simple but powerful technique that helps AI models generate text faster and more efficiently By remembering past calculations instead of repeating them, it reduces the time and effort needed to predict new words
  • KV Cache in Transformer Models - Data Magic AI Blog
    The KV Cache is a memory-efficient technique used during the inference phase of transformer-based models It stores intermediate computations of key and value vectors from the self-attention layers, avoiding redundant recalculations for previously processed tokens
  • AhaKV: Adaptive Holistic Attention-Driven KV Cache Eviction for . . .
    For example, in LLaMA-2-7B (Touvron et al , 2023b), when the input batch size is 8 and the context length reaches 32K tokens, the KV cache alone can grow to 128GB, which far exceeds the memory required to store the model parameters themselves The substantial KV cache consumption poses significant challenges for deployment on typical consumer GPUs
  • Understanding and Implementing KV Cache for Efficient LLM Inference
    KV cache eliminates redundant computations for previously processed tokens On a CPU, caching reduces latency for a 32k-token prompt from 8 minutes to 2 5 seconds
  • Transformers Key-Value Caching Explained - neptune. ai
    Key-value (KV) caching is a clever trick to do that: At inference time, key and value matrices are calculated for each generated token KV caching stores these matrices in memory so that when subsequent tokens are generated, we only compute the keys and values for the new tokens instead of having to recompute everything
  • Unlocking Longer Generation with Key-Value Cache Quantization
    Let's break it down into two pieces: kv cache and quantization Key-value cache, or kv cache, is needed to optimize the generation in autoregressive models, where the model predicts text token by token This process can be slow since the model can generate only one token at a time, and each new prediction is dependent on the previous context
  • Compressing KV cache memory by half with sparse attention
    We keep the memory tokens in the input during training and optionally add them at inference time Below we show ablations for the optimal values of M and K for the fully dense model (using same training pipeline as for dense sparse models but without introducing sparse attention at 32K length steps):





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