Optimize AI Infrastructure Into a Financial Advantage
Automatically migrate and optimize AI pipelines across frameworks and hardware — from PyTorch on GPUs to JAX on TPUs.
10–300× Faster.
90× Cheaper.
On American option pricing via Least Squares Monte Carlo, Siaivo's JAX/XLA pipeline outperforms every competing framework — on both GPU and TPU — by orders of magnitude.
Framework legend
Framework Performance Scaling on GPU (Log-Log Scale)
Framework Performance Scaling on TPU (Log-Log Scale)
Task: American option pricing via LSMC · Each batch: 50,000 simulated paths · Consistent model parameters across all environments
Automated AI Pipeline Migration
Siaivo doesn't just manage. It rewrites and validates your entire stack for the hardware that makes the most financial sense.
1. Analyze
Deep inspection of original compute graph.
2. Rewrite
Automated translation to target framework.
3. Identify Gaps
Locate architectural incompatibilities.
4. Patch Plan
Synthetic generation of missing kernels.
5. Validate
Hardware-level benchmarking & drift check.
Optimize Across Stacks,
Not Inside Them
Cross-Framework
Transition seamlessly between PyTorch, TensorFlow, and JAX without re-authoring a single line of original research code.
Cross-Hardware
Move training and inference from scarce NVIDIA A100s to readily available Google Cloud TPUs or custom silicon instantly.
Continuous Delta
The infrastructure evolves while you sleep. Siaivo identifies new optimizations as hardware firmware and drivers update.
Siaivo eliminates vendor lock-in, treating compute as a liquid commodity rather than a restricted resource.
Liquid Compute Performance
By removing the friction of manual framework porting, we unlock the true latent power of specialized hardware. Monte Carlo simulations that take days on GPUs run in minutes on optimized TPU clusters.
How It Works
Three layers of orchestration that isolate your researchers from the complexity of the metal.
Control Plane
Central Siaivo SaaS dashboard for policy management and cross-stack visibility.
Execution Agent
Lightweight binary running inside your VPC, managing real-time hardware transitions.
Human-in-Loop
Granular validation checks for edge cases requiring expert supervision.
LLM Inference: TPU vs GPU
Side-by-side benchmarks with 1,000 concurrent prompts and identical model checkpoints. Direct metrics, no post-processing.
Llama-3.1-8B
TPU v6e-1 vs NVIDIA A100
Llama-3.3-70B
TPU v6e-8 vs 2× NVIDIA H200
“AI Compute Should Be Treated Like Capital”
We are the capital allocation layer for AI infrastructure.
Most optimize inside a stack. Siaivo optimizes across them. We provide the fluid intelligence required to navigate a post-GPU world where the best hardware is the one that exists and scales today.
Born from the Giants
Our founders spent the last decade building the core infrastructure for the world's leading AI labs. We've seen the waste firsthand—and we've fixed it at scale.
Frequently Asked Questions
Everything you need to know about AI infrastructure optimization with Siaivo.
What is AI infrastructure optimization?expand_more
AI infrastructure optimization is the process of automatically migrating and tuning machine learning pipelines across frameworks and hardware — such as moving from PyTorch on NVIDIA GPUs to JAX on Google TPUs — to reduce compute costs and increase performance without manual re-engineering.
How does PyTorch to JAX migration work?expand_more
Siaivo's control layer automatically translates PyTorch model graphs into JAX-compatible representations, handles operator mapping, and validates numerical equivalence — eliminating weeks of manual porting. The migration preserves model accuracy while unlocking TPU-native performance.
How much can I save by switching from GPU to TPU?expand_more
Siaivo customers achieve up to 8× cost reduction migrating from GPU to TPU infrastructure. For LLM inference, TPU v5p delivers 1.6–2× higher throughput and 1.7–2× faster time-to-first-token compared to equivalent GPU setups, at 2–4× lower cost per million tokens.
What frameworks and hardware does Siaivo support?expand_more
Siaivo supports PyTorch and JAX as source and target frameworks. On hardware, it is fully agnostic — supporting NVIDIA GPU clusters (A100, H100), Google TPU pods (v4, v5p), and multi-cloud environments across AWS, GCP, and Azure.
How fast can Siaivo migrate an AI pipeline?expand_more
Migration is automated end-to-end. Monte Carlo simulations that take days on standard GPU infrastructure run in minutes on Siaivo-optimized TPU clusters — delivering up to 300× faster throughput with no manual intervention.
Who is Siaivo built for?expand_more
Siaivo is built for AI-first companies and research institutions spending $2M–$10M+ annually on compute infrastructure. If your team runs large-scale LLM inference, model training, or simulation workloads on GPU clusters, Siaivo converts that cost into a capital advantage.
Start Optimizing Your AI Infrastructure
Secure your slot for the Siaivo Control Layer private beta.