Search Results for "memory usage" - Page 5
Sort By:
Showing 120 open source projects for "memory usage"
View related business solutions-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
flutter_ume
UME is an in-app debug kits platform for Flutter
flutter_ume is an in-app debug-kit platform for Flutter applications, developed by ByteDance’s Flutter Infra team. It lets developers embed a suite of debugging tools directly into a Flutter app (during development or debug builds), enabling inspection, performance monitoring, UI debugging, network request inspection, widget hierarchy introspection, and more — all from within the running app. UME bundles multiple “plugin kits” (e.g., UI inspector, performance monitor, device info panel,... -
2
TerarkDB
A RocksDB compatible KV storage engine with better performance
...It aims to be drop-in compatible with existing RocksDB setups: you can migrate most RocksDB instances over to TerarkDB without rewriting your storage logic. Under the hood, TerarkDB employs advanced data structures and compression strategies to reduce I/O, memory usage, and latency variability — delivering higher throughput and more predictable performance under heavy load. Because of these optimizations, TerarkDB can be especially beneficial for services requiring fast read/write responses under variable workloads or those dealing with large datasets while aiming to keep resource usage efficient. ... -
3
TextBrewer
A PyTorch-based knowledge distillation toolkit
...It includes various distillation techniques from both NLP and CV field and provides an easy-to-use distillation framework, which allows users to quickly experiment with the state-of-the-art distillation methods to compress the model with a relatively small sacrifice in the performance, increasing the inference speed and reducing the memory usage. -
4
TurboTransformers
Fast and user-friendly runtime for transformer inference
TurboTransformers is a high-performance inference framework optimized for running Transformer models efficiently on CPUs and GPUs. It improves latency and throughput for NLP applications. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
OpenAI Glow
Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"
...The model is capable of producing high-quality synthetic images while maintaining interpretable latent spaces that enable meaningful manipulation of generated outputs. Glow’s architecture is based on reversible layers and efficient flow operations, which allow large-scale training while keeping memory usage manageable. The repository provides training code, pretrained models, and scripts for generating samples or reproducing key results from the original research. Glow is primarily intended for researchers and practitioners exploring generative modeling, likelihood-based training, and interpretable deep learning systems. -
6
Texar
Toolkit for Machine Learning, Natural Language Processing
Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation. Texar was originally developed and is actively contributed by Petuum and CMU in collaboration with other institutes. A mirror of this... -
7
FFMpegCore
A .NET FFMpeg/FFProbe wrapper for easily integrating media analysis
...FFMpegCore supports working with files and streams, enabling flexible workflows including in-memory processing. Developers can build complex pipelines using a fluent argument builder while maintaining readability and control. Overall, it serves as a powerful bridge between FFmpeg capabilities and modern .NET development environments. -
8
X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
X-DeepLearning (XDL for short) is a complete set of deep optimization solutions for high-dimensional sparse data scenarios (such as advertising/recommendation/search, etc.). XDL version 1.2 has been released recently. Performance optimization for large batch/low concurrency scenarios, 50-100% performance improvement in such scenarios. Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely... -
9
OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
Bolt ML
10x faster matrix and vector operations
...The core idea behind Bolt is to compress large collections of dense numeric vectors and perform mathematical operations directly on the compressed representations instead of decompressing them first. This approach significantly reduces both memory usage and computational overhead when working with high-dimensional data commonly used in machine learning systems. Bolt is particularly useful in applications such as similarity search, approximate nearest neighbor queries, and large-scale matrix computations where millions of vectors must be processed efficiently. The project includes algorithms designed to accelerate operations such as dot products and distance calculations, which are central to many machine learning tasks. -
11
CRFSharp
CRFSharp is a .NET(C#) implementation of Conditional Random Field
...CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. ... -
12
The Deep Email Miner Application is a software solution for the multistaged analysis of an Email Corpus. Social network analysis and text mining techniques are connected to enable an in depth view into the underlying information. The self-executable Version 1.1 jar file will now run on Java 1.5 or higher. A Windows executable file of Version 1.1 is also provided in the Files section. Documentation can be found on the project homepage.
-
13
We developed a RNA-Seq Data Annotation Pipeline named RNADAP, which measure genes expression in isoform level, work with high speed and less memory usage. Besides, our pipeline can be compatible with results from different mapping software.
-
14
Nemotron 3
Large language model developed and released by NVIDIA
...It is the post-trained and FP8-quantized variant of the Nemotron 3 Nano model, meaning its weights and activations are represented in 8-bit floating point (FP8) to dramatically reduce memory usage and computational cost while retaining high accuracy. The base Nano architecture uses a hybrid Mamba-Transformer Mixture-of-Experts (MoE) design, allowing the model to activate only a small fraction of its 31.6 billion parameters per token, which improves speed and efficiency without sacrificing quality on complex queries. ... -
15
Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
...The model contains approximately 120 billion parameters, but employs a Mixture-of-Experts architecture that activates only a smaller subset of parameters during inference, improving computational efficiency while maintaining high capability. Its architecture combines Transformer attention layers with Mamba state-space components to balance long-context reasoning, memory efficiency, and high-quality language generation. The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning. -
16
SynquoRum
Multi-AI workspace with persistent cross-session memory via MCP
...The product supports BYOK (Bring Your Own Key) across major providers, with no markup on API usage. Cross-provider memory uses pgvector with keyword fallback. The workspace ships with 664 prebuilt integrations and 32 languages. Pricing is simple. Free tier exposes the core workspace. Paid plans (Standard $25, Pro $50, Maximum $150) share the same feature set — only credits differ. No per-seat upcharges, no feature gates. -
17
Laguna XS.2
Open agentic coding model optimized for local deployment
Laguna XS.2 is Poolside’s first open-weight Mixture-of-Experts model designed specifically for agentic coding and long-horizon software engineering tasks. The model contains 33B total parameters with only 3B activated per token, allowing it to deliver strong coding performance while remaining efficient enough to run locally on modern consumer hardware. It uses a hybrid attention architecture that combines Sliding Window Attention and global attention layers, reducing memory requirements and... -
18
DiffusionGemma
NVFP4 DiffusionGemma model for fast multimodal text generation
DiffusionGemma 26B A4B IT NVFP4 is NVIDIA’s Model Optimizer quantized release of Google DeepMind’s DiffusionGemma 26B A4B IT model. It is an open-weights multimodal generative model that processes text, images, and video inputs to produce text output through discrete diffusion. Built on the Gemma 4 26B A4B Mixture-of-Experts architecture, it has 25.2B total parameters and 3.8B active parameters, balancing capability with efficient inference. Its diffusion-based generation produces tokens in... -
19
MiMo-V2.5-Pro
Flagship MoE model for long-context agents and complex coding
...The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains. Architecturally, it uses a hybrid attention system combining Sliding Window Attention and Global Attention to significantly reduce memory usage while preserving long-context performance. It also integrates multi-token prediction modules that accelerate inference and improve reinforcement learning efficiency. Trained on around 27 trillion tokens with FP8 mixed precision and refined through supervised fine-tuning, large-scale agentic reinforcement learning, and distillation. -
20
Mistral Large 3 675B Instruct 2512 NVFP4
Quantized 675B multimodal instruct model optimized for NVFP4
Mistral Large 3 675B Instruct 2512 NVFP4 is a frontier-scale multimodal Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. It retains the same instruction-tuned behavior as the FP8 model, making it ideal for production...
