Model optimization + GPU optimization. Full stack efficiency.
Most AI workloads run unoptimized models on underutilized GPUs.
FP16 everywhere. Generic kernels. No precision tuning. You're using 3x more compute than necessary.
VMs with overhead. Slow tenant spin-up. 30-50% utilization. You're paying for GPUs that sit idle.
We fix both.
We measure information content at each layer and assign precision accordingly. NVFP4 on Blackwell. Custom CUDA kernels tuned for specific architectures. Thermodynamic precision assignment.
MicroVM architecture with sub-second tenant spin-up. Custom GPU abstraction layer—bare-metal performance, full tenant isolation. Multi-tenant scheduling that packs workloads without interference.
Other platforms optimize one layer. We optimize all of them.
Not just optimized—verified.
Our core infrastructure is formally verified. Critical paths are proven correct in Lean4, with cryptographic attestation at every layer. This isn't marketing—it's math.
Trust Distance
Core theorems proven at distance ≤ 1. You know exactly how far from mathematical proof each component sits.
Critical inference paths verified with mathematical proofs in Lean4.
SHA256, ed25519, git-based audit trail at every layer.
Every operation completes cleanly or rolls back. No undefined states.
Private deployments with VPCs, audit logging, and verification proofs.
Weyl is where the breakthroughs happen. Like DeepMind to Google.
We publish papers, release open source, and push what's possible in efficient inference. The research from Weyl powers everything on Fleek.
Current work: