Isaac Gym fundamentally changes the economics of reinforcement learning for robotics by moving the entire physics simulation pipeline onto the GPU. Prior to Isaac Gym, the standard workflow involved running physics on CPU (via MuJoCo, PyBullet, or Gazebo), transferring state observations to GPU for neural network inference, then sending actions back to CPU for the next simulation step. This CPU-GPU ping-pong created a severe bottleneck, limiting researchers to tens or at most hundreds of parallel environments. Isaac Gym solves this by performing all rigid-body physics computations on the GPU using NVIDIA's PhysX engine, allowing thousands of environments to run in parallel on a single workstation GPU with minimal overhead. The performance gains are dramatic. On tasks such as humanoid walking, ant locomotion, and Franka robot manipulation, Isaac Gym can simulate over 100,000 parallel environments at interactive frame rates on a single NVIDIA RTX 3090 or A100. This massive parallelism directly translates into faster training for model-free RL algorithms like PPO and SAC. Experiments in the paper show that what used to require a cluster of CPU servers can now be done on a single desktop in a fraction of the time. For example, training a humanoid policy from scratch that previously took days on CPU clusters can be completed in under an hour on GPU. Beyond raw speed, Isaac Gym introduces a unified tensor-based API that simplifies the development of batched RL environments. Observations, actions, rewards, and dones are all represented as PyTorch tensors living in GPU memory, making it trivial to plug in modern deep learning frameworks without any data marshalling code. The system also supports direct force and contact sensor readings, domain randomization for sim-to-real transfer, and integration with the USD scene description format for complex environment authoring. The physics solver is deterministic and reproducible, which is crucial for debugging RL algorithms and comparing research results. The release of Isaac Gym (and its successor Isaac Sim) has had an outsized impact on the robotics RL community. It enabled projects like Legged Gym, OmniDrones, and many dexterous manipulation benchmarks that would have been computationally infeasible under the old CPU-centric paradigm. The paper benchmarks a wide range of classic control and robotics tasks, demonstrating that GPU simulation does not sacrifice physical accuracy compared to CPU engines for the regimes relevant to RL. For any lab working on policy learning for locomotion or manipulation, understanding and using Isaac Gym is now essentially a prerequisite.