Stop losing 10% of your GPUs to training failures.
When AI workloads fail, we recover them automatically — or pinpoint the exact root cause.
A bit flip, a network timeout, a race. Merge the verified state and continue from the failing step. No checkpoint rollback.
A logic bug, memory corruption, bad data. Attribute it to software, data, or hardware. Emit the causal chain and a fix.
between failures on a 1,000-GPU cluster
unexpected interruptions in 54 days of Llama 3 training
lost per year to 10% waste on 10,000 GPUs
Sources: Meta, The Llama 3 Herd of Models (2024), §3.3.4. Estimate: 10,000 GPUs × 8,760 h/year × 10% × $5/GPU-h = $43.8M.
The Problem
The memory wall makes training failures more costly
Growing demand adds system complexity, making training more error-prone and harder to diagnose.
10% waste ≈ $44M for 10,000 GPUs per year
Meta: 419 unexpected interruptions in 54 days of Llama 3 training.
Source: Meta, The Llama 3 Herd of Models (2024), §3.3.4. Estimate: 10,000 GPUs × 8,760 h/year × 10% × $5/GPU-h = $43.8M.
Two ways a run fails
Training stops
Crash · Hang · Out-of-memory
Training goes wrong silently
Silent corruption · Loss spike · Misconfiguration
Checkpoint-restart is blind
The only answer today is full rollback and restart. It cannot tell a GPU bit flip from a logic bug, it torches up to a checkpoint interval of compute every time, and it never says why.
Our Platform
Three line items on one savings report
Before the run, during the run, every run. The same recording layer pays for itself three ways.
Preflight check catches doomed configs in minutes
OOM-bound setups · wrong parallelism · precision & topology mismatches
XScopeFast recovery · auto-diagnosis
GPU waste from loud and silent failures cut from ~10% to 1–2% (projected)
WatcherGPU + CPU memory diagnostics
Better memory policy · right-sized parallelism · less memory consumption
XScopeAll three land on one savings report. Preflight, recovery, and memory diagnostics, from the same recording layer.
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.
Always-on recording
Always-on selective recording at under 5% overhead in production. One recording feeds both recovery and diagnosis.
Replay: recover or diagnose
On failure, deterministic replay classifies it. Transient faults recover automatically; persistent bugs produce a full causal chain.
Why Teyon
Each one alone is a moat. Together they are a fortress.
Zero public training data, foundational IP, and a decade of compounding research advantage at the most selective systems conferences in the world.
Three-year competitor lag
A competitor starting today arrives after our ecosystem is locked.
Patent-protected IP
Foundational IP on deterministic replay and automated root-cause analysis.
Deep tech foundation
Zero public training data. LLMs cannot auto-generate the engine, even with massive effort.
foundation ↑ compounding
Ready for resilient AI infrastructure?
Stop losing $10K+ per GPU failure. Let's talk.