Search Results for "cache memory simulator"
Sort By:
Showing 255 open source projects for "cache memory simulator"
View related business solutions-
Earn up to 16% annual interest with Nexo.Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
lru-cache
A fast cache that automatically deletes the least recently used items
node-lru-cache is a high performance in memory caching library for Node.js that automatically evicts the least recently used items when the cache reaches its size limit. It is designed to provide predictable memory usage while maintaining fast lookup and update operations. The library is widely used in server side JavaScript applications where efficient caching can significantly improve performance. -
2
Cache
Nothing but Cache
Cache doesn't claim to be unique in this area, but it's not another monster library that gives you a god's power. It does nothing but caching, but it does it well. It offers a good public API with out-of-box implementations and great customization possibilities. Cache utilizes Codable in Swift 4 to perform serialization. The cache is built based on a Chain-of-responsibility pattern, in which there are many processing objects, each knows how to do 1 task and delegates to the next one, so can... -
3
Windows Memory Cleaner (WMC)
Free RAM cleaner uses native Windows features to optimize memory areas
Windows Memory Cleaner (aka WMC or WinMemoryCleaner) is a lightweight, portable Windows utility designed to optimize system memory using native Windows API functions. It provides a transparent and user-friendly interface for freeing up RAM that is often held unnecessarily by cached data or inefficient processes. Unlike many “RAM booster” tools, it relies entirely on documented system methods rather than hidden tricks or unsafe optimizations. The application can automatically clean memory... -
4
MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
MSA, or Memory Sparse Attention, is a research framework for scaling language-model memory to extremely long contexts. It replaces full attention over all tokens with sparse selection of compressed latent memory states. Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
Gocache
Go cache library that brings you multiple ways to manage caches
Guess what is Gocache? a Go cache library. This is an extendable cache library that brings you a lot of features for caching data. Multiple cache stores: actually in memory, redis, or your own custom store. A chain cache: use multiple cache with a priority order (memory then fallback to a redis shared cache for instance) A loadable cache: allow you to call a callback function to put your data back in cache. ... -
6
Ristretto
A high performance memory-bound Go cache
Ristretto is a fast, concurrent cache library built with a focus on performance and correctness. It all started with needing a memory-bound, concurrent Go cache in Dgraph. We looked around for a solution, but we couldn’t find a great one. We then tried using a sharded map, with shard eviction to release memory, which caused us memory issues. We then repurposed Groupcache’s LRU, using mutex locks for thread safety. -
7
JetCache
JetCache is a Java cache framework
JetCache is a Java cache abstraction that provides uniform usage for different caching solutions. It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
8
Nebulex
In-memory and distributed caching toolkit for Elixir
Nebulex provides support for transparently adding caching into an existing Elixir application. Similar to Ecto, the caching abstraction allows consistent use of various caching solutions with minimal impact on the code. Nebulex cache abstraction shields developers from directly dealing with the underlying caching implementations, such as Redis, Memcached, or even other Elixir cache implementations like Cachex. Additionally, it provides totally out-of-box features such as cache usage... -
9
BigCache
Efficient cache for gigabytes of data written in Go
...Fast, concurrent, evicting in-memory cache written to keep big number of entries without impact on performance. BigCache keeps entries on heap but omits GC for them. To achieve that, operations on byte slices take place, therefore entries (de)serialization in front of the cache will be needed in most use cases. -
Veeam Data Platform v13.1Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
10
R-KV
Redundancy-aware KV Cache Compression for Reasoning Models
R-KV is an open-source research project that focuses on improving the efficiency of large language model inference through key-value cache compression techniques. Modern transformer models rely heavily on KV caches during autoregressive decoding, which store intermediate attention states to accelerate generation. However, these caches can consume large amounts of memory, especially in reasoning-oriented models with long context windows. R-KV introduces a method for compressing the KV cache during decoding, allowing models to maintain reasoning performance while reducing memory consumption and computational overhead. ... -
11
DeepSeek-Reasonix
DeepSeek-native AI coding agent for your terminal
DeepSeek Reasonix is a DeepSeek-native AI coding agent designed for terminal-based software development. It is built around prefix-cache stability, which helps reduce token costs during long sessions and allows users to leave the agent running across extended workflows. Reasonix includes a coding mode with filesystem and shell tools, a lighter chat mode, one-shot task execution, health checks, session utilities, and project-scoped memory. It supports reviewed SEARCH/REPLACE edits, plan mode, MCP servers, web search, hooks, skills, semantic indexing, transcript replay, event logs, and cost or cache tracking. ... -
12
Kimi K3 in C
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU
Kimi K3 in C is a portable C99 inference engine built to run the 2.78-trillion-parameter Kimi K3 model on CPUs without BLAS, machine-learning frameworks, or GPUs. It demonstrates inference from a roughly 1.56 TB checkpoint with measured memory use as low as 8.24 GB. The runtime streams model trunk layers and routed experts from disk instead of keeping all weights resident in memory. Memory presets balance pinned layers and an expert LRU cache for laptops, desktops, workstations, and servers. Incremental generation preserves KV cache and recurrent state between tokens. ... -
13
Redis
An in-memory database that persists on disk
Redis is an open-source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions, and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster. ... -
14
KVCache-Factory
Unified KV Cache Compression Methods for Auto-Regressive Models
KVCache-Factory is an open-source research framework designed to explore and implement unified key-value cache compression techniques for autoregressive transformer models. In large language models, the key-value cache stores intermediate attention states that enable efficient token generation during inference, but these caches can consume large amounts of GPU memory when handling long contexts. KVCache-Factory provides a platform for implementing and evaluating multiple compression strategies that reduce memory usage while preserving model performance. ... -
15
FusionCache
FusionCache is an easy to use, fast and robust cache
FusionCache is an easy-to-use, fast, and robust cache with advanced resiliency features and an optional distributed 2nd layer. It was born after years of dealing with all sorts of different types of caches: memory caching, distributed caching, http caching, CDNs, browser cache, offline cache, you name it. So I've tried to put together these experiences and came up with FusionCache. -
16
Dao Code
Open-source TypeScript terminal coding agent for DeepSeek-V4
Dao Code is an open-source terminal-native AI coding assistant built around DeepSeek V4 and a cost-conscious agent architecture. It reads code, writes code, runs commands, fixes bugs, and streams tool usage inside the terminal. The project emphasizes byte-stable prompts, prefix-cache reuse, and low-cost reflection or memory forks so longer coding sessions can remain affordable. It also includes cross-session memory that verifies saved facts against the current codebase instead of blindly trusting old context. Dao Code supports approval gates, slash commands, skills, MCP tools, hooks, custom subagents, permissions, crash recovery, shadow-git checkpoints, and autonomous long-task mode. ... -
17
Nuke
Image loading system
...Nuke has an automated test suite 2x the size of the codebase itself, ensuring excellent reliability. Every feature is carefully designed and optimized for performance. Fast LRU memory cache, native HTTP disk cache, and custom aggressive LRU disk cache. Customize image pipeline using built-in Alamofire, Gifu, FLAnimatedImage, WebP plugins or create your own. Enable progressive decoding with a single line of code. Nuke supports progressive JPEG out of the box, and WebP via a plugin built by the community. Automatically prefetch images ahead of time using either Preheat or native table and collection view prefetching APIs. -
18
SD WebImage
Asynchronous image downloader with cache support
This library provides an async image downloader with cache support. For convenience, we added categories for UI elements like UIImageView, UIButton, MKAnnotationView. SD is the prefix for Simple Design (which is the team name in Daily Motion company from the author Olivier Poitrey) Image formats supported by Apple system (JPEG, PNG, TIFF, BMP, ...), including GIF/APNG animated image. In order to keep SDWebImage focused and limited to the core features, but also allow extensibility and custom... -
19
TurboFieldfare
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
TurboFieldfare is a custom Swift and Metal runtime for running the instruction-tuned Gemma 4 26B-A4B model on Apple Silicon Macs with limited memory. Instead of loading the entire model, it keeps the shared core and KV cache in RAM while streaming only the routed experts required for each token from SSD. This approach reduces active memory use to roughly 2 GB while the installed model occupies about 14.3 GB of storage. The project includes a native Mac application, command-line tools, a streaming installer, a Swift library, and an experimental OpenAI-compatible local server. ... -
20
Caffeine
A high performance caching library for Java
Caffeine is a high performance Java caching library providing a near optimal hit rate. A Cache is similar to ConcurrentMap, but not quite the same. The most fundamental difference is that a ConcurrentMap persists all elements that are added to it until they are explicitly removed. A Cache on the other hand is generally configured to evict entries automatically, in order to constrain its memory footprint. In some cases a LoadingCache or AsyncLoadingCache can be useful even if it doesn't evict entries, due to its automatic cache loading. ... -
21
Olric
Distributed, in-memory key/value store and cache
A lightweight, distributed in-memory data store designed for key-value caching and ephemeral storage. -
22
Intel(R) RDT Software Package
User space software for Intel(R) Resource Director Technology
Intel CMT CAT is a user-space software package providing support for Intel's Resource Director Technology (RDT). It offers tools and libraries to monitor and control cache allocation and memory bandwidth. -
23
Fresco
An Android library for managing images and the memory they use
Fresco’s image pipeline will load images from the network, local storage, or local resources. To save data and CPU, it has three levels of cache; two in memory and another in internal storage. Fresco’s Drawee shows a placeholder for you until the image has loaded and then automatically shows the image when it arrives. When the image goes off-screen, it automatically releases its memory. A decompressed image - an Android Bitmap - takes up a lot of memory. This leads to more frequent runs of the Java garbage collector. ... -
24
LMCache
Supercharge Your LLM with the Fastest KV Cache Layer
...These capabilities aim to lower latency, cut GPU cycles, and stabilize performance for production workloads with overlapping prompts or retrieval-augmented contexts. The end result is a cache fabric for LLMs that complements engines rather than replacing them. -
25
Kimi Linear
An expressive, efficient attention architecture
Kimi Linear is a hybrid linear attention architecture developed for efficient language modeling across short, long-context, and reinforcement learning workloads. Its core mechanism, Kimi Delta Attention, refines the gated delta rule with fine-grained controls for managing finite-state recurrent memory. The architecture combines KDA and global Multi-head Latent Attention layers at a 3:1 ratio to preserve model quality while reducing memory demands. Released Base and Instruct checkpoints contain 48 billion total parameters, activate 3 billion parameters per token, and support contexts up to one million tokens. The models were trained on 5.7 trillion tokens and can reduce KV cache requirements by up to 75 percent. ...
