Resilient AI Infrastructure
When AI workloads fail, we recover them automatically — or pinpoint the exact root cause.
Built by researchers and engineers from
The Core Problem
The Fragility of AI Infrastructure
A 1,000-GPU cluster fails every ~8 hours, wasting $10K+ in compute each time. This bleeding won't stop on its own.
The Culprits
Transient Faults: GPU bit flips, memory-bus timeouts, network jitter, race conditions — vanish on restart. Persistent Bugs: memory corruption, logic bugs — persist on restart.
Checkpoint-Restart Is Broken
The only answer today? Full rollback and restart — blind to failure types, torching 30 minutes of compute, with no fast recovery from transient faults and no root cause for persistent bugs.
Our Platform
Uniform Recording, Two Tracks
One always-on recording layer. Two uses of the same data: automatic recovery or root-cause diagnosis.
Watcher
Always-On Recording
Extensible API interception across system calls, synchronizations, PyTorch, and CUDA APIs. <5% overhead, no custom hypervisor, no deployment changes.
Deterministic Replay
Recovery & Diagnosis
Track A — Recovery: Transient fault detected? Merge state and continue without restart.
Track B — Diagnosis: Persistent bug? Replay reproduces the error and emits the full causal chain.
MemScope
GPU-Memory Semantic Analysis
Maps GPU memory back to Python objects and source lines. Understands memory faults — OOM, configuration failures, causes of high volume. Traces a PyTorch GPU-memory leak to the exact tensor and source line that created it.
How It Works
Zero Friction Deployment
Deploy across your entire infrastructure without touching a single line of code.
API Interception
Intercepts system calls, synchronizations, PyTorch, and CUDA APIs. No recompilation, no custom hypervisor, no deployment changes.
Record Everything
Always-on deterministic recording at <5% overhead in production. One recording feeds both recovery and diagnosis.
Replay → Recover or Diagnose
On crash, deterministic replay classifies the failure. Transient faults recover automatically; persistent bugs produce a full causal chain.
Proven Science
Rooted in World-Class Research
Our technology isn't built on hype—it's built on a decade of peer-reviewed research published at the most selective systems conferences in the world.
Dthreads: Efficient Deterministic Multithreading
ACM Symposium on Operating Systems Principles
DoubleTake: Fast and Precise Error Detection via Evidence-Based Dynamic Analysis
International Conference on Software Engineering
POMP: Postmortem Program Analysis with Hardware-Enhanced Post-Crash Artifacts
USENIX Security Symposium
iReplayer: In-Situ and Identical Record-and-Replay for Multithreaded Applications
ACM SIGPLAN Conference on Programming Language Design and Implementation
Watcher: In-Situ Failure Diagnosis
ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications
About Us
Building Resilient AI Infrastructure
Teyon was founded by systems researchers and engineers with decades of combined experience in deterministic replay, GPU systems profiling, and production infrastructure. We're on a mission to make AI infrastructure resilient.
Years SOSP/PLDI
Downtime Cost Saved
Runtime Overhead
Ready for resilient AI infrastructure?
Stop losing $10K+ per GPU failure. Let's talk.
No commitment required. Let's discuss your infrastructure challenges.