Alternatives

Products that do what ScalingOpt does

Efficient AI & Optimization Community

1
ZeroGPU▲309
The compute efficient layer for AI inference
Jun 2026 · zerogpu.ai
2
Tensorflow Research Cloud▲288
Accelerating open machine learning research with Cloud TPUs
2017
3IV
I've built a locally running Perplexity clone▲669
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com
4
Gradient 1.0▲92
Build and scale real-world machine learning applications
2021
5
GPT-4.5▲511
The largest and best model for chat yet in GPT family
2025
6
OpenAI Universe▲329
Platform for measuring and training AI agents
2016
7
Netmind Power▲272
The decentralised machine learning and AI platform
2023
8
GPT-5.5 by OpenAI▲477
OpenAI's smartest and most intuitive to use model yet
Apr 2026 · openai.com
9
DeepSeek-V4▲394
The open-source era of 1M context intelligence
Apr 2026 · huggingface.co
10
GPT‑5.4▲475
OpenAI's most efficient model: less tokens, more clarity
Mar 2026 · openai.com
11
Scaley▲79
Super simple web image optimization
2017
12
DeepSeek-V3.2-Exp▲155
Long-context efficiency with DeepSeek Sparse Attention
Sep 2025
13BG
Boldly go where Gradient Descent has never gone before with DiscoGrad▲232
Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
2024 · github.com
14DA
DeepForge – A Modern Development Environment for Deep Learning▲108
2017 · deepforge.org
15
Spell + Weights & Biases▲84
Accelerating deep learning experimentation
2019
16ET
End-to-end deep learning experimentation platform for Tensorflow▲151
2017 · github.com
17FD
Fast Deep Reinforcement Learning Course▲148
I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…
2022 · courses.dibya.online
18
TensorPort▲143
Help teams apply machine learning to real-world applications
2017
19DG
Dockerized GPU Deep Learning Solution (Code and Blog and TensorFlow Demo)▲80
2016 · github.com
20
TeamGPT▲122
Your ultimate go-to AI workspace tool
2023
21
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018
22IS
I started a repo for sharing algorithm implementations▲52
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
23
Minimal LLM Post-Training Experiments on an 8GB GPU (SFT, DPO, GRPO)▲21
Minimal, readable LLM post-training experiments on one 8GB GPU. Measures forgetting, seed variance, and RL emergence. - pochenai/nano-llm-posttraining
Aug 2026 · github.com
24IW
I wrote an autodiff in C++ and implemented LeNet with it▲36
2024 · gitlab.com

Ranked by how close each launch is in meaning, then by votes. Refine with a description →