Serve
In developmentMogg
A native CUDA runtime for in-house model serving
Hand-tuned CUDA kernels and KV caching, for teams who want inference running on their own hardware instead of someone else's API. Packaged builds are still coming — reach out below if you want early access.
Native CUDA · runs entirely on your own hardware
What's under the hood
Built around a resident-weights model class with request-scoped KV allocation — the same shape as production serving runtimes, not a research demo.
Custom CUDA kernels
Hand-optimized kernels for matrix operations, attention mechanisms, and tensor computations.
Advanced KV caching
A multi-level cache system with a rolling window cache for efficient memory usage on long contexts.
Tensor core utilization
Uses NVIDIA tensor cores for accelerated mixed-precision computation.
Multi-model support
Runs LLaMA, Qwen, and Muse Glimmer on the same runtime, including Mixture-of-Experts (MoE) architectures with expert routing.
Mixed precision
FP16 / BF16 support for improved throughput and memory efficiency.
Memory pool management
Efficient GPU memory allocation and reuse, tuned for sustained serving workloads rather than one-off runs.
Let's talk about your agents
Whether it's agent memory, a no-code build, or in-house model serving, tell us what you're working on and we'll point you to the right place to start.
Free Architecture Review
We'll look at your current agent setup and flag where memory, tooling, or serving is the bottleneck
Custom Deployment Guidance
Tailored recommendations for your use case, team size, and hardware
Enterprise Support Options
24/7 support, priority updates, and dedicated engineering resources
Built for teams running agents in production
From open-source Lint-AI users to enterprises deploying custom inference — RooAGI covers the whole stack.