When a CUDA kernel produces a numerical singularity, NoNans intercepts the event at the kernel boundary, resolves it inside our framework, and returns a finite, optimizer-coherent tensor to the GPU. Training continues at the next step. No rollback. No checkpoint reload. No code change.
IEEE 754 has no defined value for a class of operations that occur regularly in modern training: division by zero in attention, softmax overflow at long context, gradient accumulation past representable range. When these occur, the standard practice is to roll back to the last checkpoint, discarding hours of compute. NoNans operates one layer below: at the kernel boundary, where the singularity originates, before propagation begins.
The layer sits between PyTorch (or any framework that ultimately calls CUDA) and your GPU. On the normal path, it does nothing measurable. When a kernel produces a numerical singularity, NoNans intercepts the event, hands it to the resolution runtime, receives a finite tensor back, and returns it to the GPU. Three components, two boundaries, one new behavior at the moment of failure.
The detection layer is identical across tiers. The resolution mode determines deployment topology, latency profile, and pricing. Choose the depth that matches your trust model, network constraints, and contract scale.
Removing numerical singularities as a constraint changes which training regimes are viable in production. Each of the four below is a regime that frontier teams currently avoid or carefully babysit. With the layer underneath, they become routine.
FP8 is roughly twice as fast as BF16 on H100 and Blackwell. Almost nobody runs it for full training because the loss-scaling dance is fragile and one bad batch ends the run. With NoNans in place, FP8 stops being a research demo and becomes the default precision.
High learning rates produce gradient explosions that birth NaNs. Frontier teams babysit warmup schedules to avoid them. With a stable layer below, the warmup loosens, the schedule simplifies, and convergence accelerates measurably.
Standard softmax attention denominators collapse at sequence lengths the industry races toward. NoNans holds at million-token context where attention scores would otherwise produce singularities, both in training and inference.
Reinforcement learning is the most numerically unstable training regime in modern ML. KL explosions, reward variance, gradient norms swinging across orders of magnitude in a single step. Our layer holds where stock PyTorch loses 40% of runs.
The same primitive deployed across distinct buyer pains. Each surface has its own customer profile, pricing logic, and sales cycle. The training and inference surfaces are the wedge; the others compound as the deployment base grows.
Measured on H100 SXM5 clusters running 70B+ parameter training runs. Methodology and replication kit available under MNDA. Public benchmark for the headline scenarios is reproducible by anyone with H100 access.
The mechanism is patent pending and lives only on our servers (or, for Enterprise on-prem, inside compiled binaries under contract). The system around it compounds with every deployment.
Free tier covers experimentation up to 8 GPUs. Pro is usage-based with no seat fees, scaling with your cluster. Enterprise is contracted, SLA-backed, and supports on-premise and source-escrow terms.
Doctoral researcher and computer scientist operating at the confluence of advanced mathematics, artificial intelligence, and systems design. Author of the resolution framework that powers NoNans, validated on production neural networks and large language models.
Open to ML infrastructure leads, technical founders, and pre-seed investors who understand that compute waste is the largest controllable cost in frontier AI.