The Science of
Training Intelligence.
Stop burning money on idle GPUs. Training OS uses self-optimizing kernels, roofline-guided acceleration, and federated learning to achieve 40-70% MFU improvements—automatically.
Roofline-Guided Optimization
Know Your Bottleneck. Fix It Automatically.
Most teams guess why their training is slow. Metis Prism uses Roofline Model Analysis to pinpoint whether you're memory-bound or compute-bound—then applies the right optimization automatically.
Memory-bound? We enable kernel fusion. Compute-bound? We tune precision and parallelism. No guesswork. Just science.
Metis Prism Hive
Federated Kernel Intelligence.
Every optimization your cluster discovers becomes a "Winning Config" that propagates across your infrastructure. Enable Opt-in Global Mode to benefit from the collective intelligence of the entire Metis Prism community.
When someone running Llama-3 on H100s finds a 30% speedup, you get it too—automatically validated and broadcast to your clusters.
# Git-like Kernel Management $ prism kernel branch create experiment/fp8-attention [+] Created: experiment/fp8-attention [+] Base: main@v1.2.3 $ prism kernel experiment run --branch experiment/fp8-attention [+] Running A/B comparison... [+] Main: 1.2 TFLOPS | FP8 Branch: 1.8 TFLOPS [+] Winner: experiment/fp8-attention (+50%) $ prism kernel merge experiment/fp8-attention --to main
Reproducible Science
Treat Kernels Like Code.
Experiment fearlessly. Every kernel configuration gets full version history, branching, and rollback. Broken optimization? Roll back in seconds. Found a winner? Merge it to production.
Run A/B experiments between kernel branches. Let the data decide which configuration wins.
# Metis Prism Kernel Dispatch > Hardware: NVIDIA H100 SXM5 > Workload: Training (Llama-3-70B) [+] Roofline Analysis: Memory-Bound [+] JIT Compilation: ENABLED (Auto-Tuned) [+] Kernel Fusion: ENABLED (Reducing BW pressure) [+] Precision: FP8 (Tensor Core optimal) >>> MFU: 42% -> 71% (+69% improvement) >>> Cost Reduction: $847/day saved
Self-Optimizing Kernels
Micro-Optimized at the Metal.
Generic kernels leave 30-50% performance on the table. Metis Prism's Kernel Intelligence Layer continuously profiles, tunes, and optimizes—without touching your code.
- ✓Architecture-Aware Adaptation: Learns from your specific hardware to improve over time.
- ✓Automated Fusion: Collapses ops to reduce memory bandwidth pressure.
- ✓Predictive Safety: Prevents thermal throttling and OOMs before they happen.
Foresight Predictions
Stop guessing. Predict costs, performance, and failures before you provision a single GPU.
Pre-Flight Costing
Know exactly what your training run will cost. Get confidence intervals, not estimates. Our models are trained on millions of real workloads.
Explainable Predictions
Every prediction comes with full Glassbox explainability. See why we predicted what we predicted—no black boxes.
Failure Prevention
Predict OOMs, gradient explosions, and hardware failures hours before they happen. Save runs, not regrets.
Green AI Dashboard
Sustainability Meets Performance.
Every kernel optimization doesn't just save money—it saves the planet. Training OS calculates the carbon impact of your workloads and shows you exactly how much CO₂ you're saving.
Built for ESG compliance. Export sustainability reports that translate GPU hours into real-world impact—equivalent trees planted, carbon offset, and annualized savings.
- ✓Carbon Footprint Tracking: Per-workload CO₂ calculations based on region and hardware.
- ✓ESG Reports: One-click sustainability reports for investors and compliance.
- ✓Annualized Projections: See your yearly savings in dollars and carbon.
# Foresight-Guided Adaptive Distribution > Hardware: 128x H100 Cluster > Initial Strategy: PyTorch FSDP [+] Foresight Alert: Network degradation detected between rack 4 and 5 [+] Risk: Straggler nodes will stall all-reduce operations [+] FgAD Engine: Computing optimal fallback topology... [+] Transitioning Strategy: FSDP -> Swarm (Asynchronous) [+] State Transfer: Zero-copy in memory (Sub-2s) >>> Status: Job continues without interruption.
Open Sourcing FgAD
Mid-Training Strategy Switching.
Training strategies used to be fixed at launch. We are open-sourcing the core telemetry and adapters for Foresight-Guided Adaptive Distribution (FgAD), allowing you to dynamically adapt topologies on the fly.
When hardware conditions change (thermal throttling, node failures), FgAD uses our enterprise control plane to seamlessly switch your running job between FSDP, Pipeline, or Swarm parallelism—without stopping the training loop and with zero state loss.
Any Framework. Any Silicon.
The Intelligence OS abstracts hardware complexity. Write standard PyTorch or JAX, and let us handle the optimizations—from H100 to TPU to Trainium.
🏗️ Framework Agnostic
First-class support for the entire ecosystem. Optimizations work across all frameworks—no code changes required.
💾 Silicon Agnostic
One optimization layer, every accelerator. We maintain kernel profiles for all major silicon.
Kernel Marketplace
Publish your optimized kernels. Monetize your ML engineering expertise. Discover community-validated optimizations for your specific workloads.
Publish
Share your winning kernel configs with the community or keep them private.
Monetize
Set your price. Earn 70% of every download. Turn expertise into revenue.
Discover
Find optimizations for Llama, Mistral, GPT—verified by real workloads.
Ready to Train Smarter?
Stop leaving performance on the table. Training OS learns from every workload to make your next run faster, cheaper, and greener.