KV Cache from scratch in nanoVLM
This Hugging Face blog post walks through implementing a key-value (KV) cache from scratch within the nanoVLM framework, a minimal vision-language model codebase. The post serves as a technical tutorial explaining how KV caching works in transformer-based multimodal models and how to integrate it for inference efficiency. It targets practitioners seeking to understand the mechanics of KV caching in the context of VLMs rather than just using it as a black box.
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Unlocking Longer Generation with Key-Value Cache Quantization
This Hugging Face blog post covers KV cache quantization as a technique to reduce memory consumption during LLM inference, enabling longer context generation without proportional VRAM increases. The post likely explains how quantizing the key-value cache (e.g., to INT8 or lower precision) trades minimal accuracy for significant memory savings. This is directly relevant to inference efficiency and long-context deployment patterns.
Mastering Long Contexts in LLMs with KVPress
NVIDIA and Hugging Face present KVPress, a library for compressing the KV cache in large language models to enable more efficient long-context inference. The tool implements multiple KV cache compression ("pressing") algorithms that reduce memory footprint and latency without retraining models. KVPress is positioned as a practical toolkit for deploying LLMs in long-context scenarios where KV cache size becomes a bottleneck.
nanoVLM: Minimal Pure-PyTorch Repository for Training Vision-Language Models
Hugging Face published nanoVLM, a minimal open-source repository designed to make training vision-language models (VLMs) as simple as possible using pure PyTorch. The project aims to lower the barrier to entry for VLM research and experimentation by providing a clean, readable codebase without heavy abstractions. It follows in the tradition of educational ML repositories like nanoGPT, targeting researchers and practitioners who want to understand or customize VLM training from scratch.
LMCache: KV cache layer for LLM inference acceleration
LMCache is an open-source Python library providing a KV cache layer designed to accelerate LLM inference. The project has accumulated 8,613 GitHub stars with modest daily growth (+17). It targets inference efficiency by offloading or sharing KV cache state across requests.
VaSE: Value-Aware Stochastic KV Cache Eviction improves reasoning model efficiency
A new arXiv preprint introduces Value-aware Stochastic KV Cache Eviction (VaSE), a training-free method for compressing KV caches in long-chain-of-thought reasoning models. The authors identify two key failure modes in prior eviction approaches — catastrophic repetition loops caused by evicting high-magnitude value states, and low cache diversity — and address both with targeted protections and stochastic eviction. On six reasoning tasks with Qwen3 models at 4x compression, VaSE outperforms the current best selection-based sparse attention method and exceeds the strongest eviction baseline by over 4%, while supporting FlashAttention2 and maintaining a static memory footprint.
KVEraser: Learned KV cache editing for efficient localized context erasing in LLMs
KVEraser is a learned method for efficiently erasing specific spans from an LLM's KV cache without full recomputation of subsequent tokens. The approach replaces only the KV states of the erased interval with learned steering states, using a two-stage training pipeline of generic pre-training followed by task-specific fine-tuning. On contexts from 1K–32K tokens, KVEraser nearly matches full recomputation quality while incurring only 24% latency overhead versus a 17.6x increase for exact recomputation, with demonstrated generalization to long-document QA with harmful factual distractors.
Latent Context Language Models (LCLMs) achieve competitive encoder-decoder KV cache compression at scale
Researchers introduce Latent Context Language Models (LCLMs), a family of encoder-decoder compressors that map long token sequences to shorter latent embeddings consumed by a decoder, targeting the KV cache memory bottleneck in long-context inference. The authors conduct architecture search and continually pre-train 0.6B-encoder/4B-decoder models on over 350B tokens at compression ratios of 1:4, 1:8, and 1:16. LCLMs improve the Pareto frontier across general-task performance, compression speed, and peak memory, and are demonstrated as efficient backbones for long-horizon agents that can skim compressed context and expand relevant segments on demand. The work closes a previously noted gap between encoder-decoder approaches and KV cache compression methods on the accuracy-efficiency frontier.
SmolVLM - Small Yet Mighty Vision Language Model
Hugging Face introduces SmolVLM, a compact vision-language model designed to deliver strong multimodal performance at small parameter counts. The model targets edge and resource-constrained deployment scenarios while maintaining competitive capabilities relative to its size. The announcement highlights efficiency improvements in both training and inference for small-scale VLMs.