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Can I use RightNow AI to profile and optimize CUDA kernels for specific GPU hardware?

Yes. RightNow AI includes a CUDA editor with profiling tools and PTX/SASS inspection, letting you examine kernel performance at a low level. You can also benchmark your code across different hardware variants to see how kernels behave on the GPUs you actually deploy to.

Does RightNow AI help reduce inference latency in production LLM deployments?

That is a primary use case. RightNow AI offers token-level inference efficiency optimization, sparse attention acceleration, and kernel-level runtime tuning. You can manage inference workloads on the platform's distributed GPU infrastructure and get telemetry on what is actually happening during execution.

What is GPU emulation in RightNow AI used for?

GPU emulation allows you to develop and test CUDA code without having the physical hardware on hand, and to run remote GPU workflows. This is useful for iterating quickly during development before you deploy to your production GPU cluster.

Does RightNow AI automate kernel optimization or synthesis?

Yes, the platform includes agents that can perform automated GPU-kernel search and synthesis, reducing the manual effort of hand-tuning kernels. The agent infrastructure is available as open source, so you can adapt it to your own optimization pipelines.