Official AI Systems Overview
Blazil AI infrastructure runs on the same transport core as the ledger.
This page exists as the official AI infrastructure entry point for Blazil. It is separate from the homepage so search engines and evaluators can treat the AI stack as a first-class product surface rather than just an on-page section.
Blazil extends the same zero-copy discipline, observability, and operator model used in the financial infrastructure into AI workloads: dataset transport, ONNX inference, distributed LLM orchestration, and production-oriented runbooks.
Inference path
Tract ONNX + io_uring dataloader
Pure-Rust serving path without Python or GPU runtime assumptions.
Dataset coverage
5 production-grade dataset types
Text, time series, feature vectors, audio, and object detection workloads.
Verified LLM run
Qwen2.5-7B · 32 tokens in 19.7s
Distributed 3-stage Aeron IPC pipeline verified on Apple M4 CPU.
AI Infrastructure
The same zero-copy discipline now powers datasets, inference, and distributed LLM serving.
Blazil is no longer just a high-throughput ledger engine. It now extends the same transport, observability, and operational model into AI workloads: Tract ONNX inference, io_uring data loading, five production-grade dataset types, and a verified Qwen2.5-7B distributed pipeline.
Pure Rust Inference
Tract ONNX inference and io_uring data loading remove Python and GPU runtime assumptions from the serving path.
Dataset-Agnostic Transport
All workloads flow through the same Sample<Vec<u8>> pipeline, so text, time-series, audio, and vision reuse the proven transport layer.
Distributed LLM Pipeline
Qwen2.5-7B is already running through a 3-stage Aeron IPC pipeline with token feedback, KV cache continuity, and multi-token generation.
Operator-Ready Foundations
Security, observability, and supply-chain controls are shared across fintech and AI workloads instead of being bolted on later.
Dataset Coverage
AI Metrics
Inference RPS
1,500–2,000
AWS i4i.4xlarge
Tract ONNX, CPU-only estimate pending production benchmark
Cost per RPS
$0.54–0.72/RPS/mo
AWS i4i.4xlarge
Comparable small-scale efficiency without GPU fleet overhead
Verified LLM run
32 tokens in 19.7s
Apple M4 CPU
Qwen2.5-7B-Instruct on distributed 3-stage pipeline
Why this matters
The README now positions Blazil as financial and AI infrastructure. The site needs to show that the AI side is already concrete: datasets are implemented, inference plumbing exists, and the distributed LLM path is verified rather than hypothetical.
Where to go next
Docs
Open operator docs, AI systems notes, and dataset details.
Architecture
See how AI shares transport, routing, and replication primitives.
Roadmap
Review AI milestones from Tract ONNX to large-model production serving.
AI FAQ
What is Blazil AI infrastructure?
Blazil AI infrastructure is the inference and dataset-serving layer built on the same transport and operational primitives as the transaction engine. It includes Tract ONNX inference, io_uring data loading, and a verified distributed Qwen2.5 pipeline.
Is the AI stack separate from the financial infrastructure?
No. The point of Blazil is that the AI stack reuses the same transport discipline, observability, security controls, and deployment model as the financial system instead of being bolted on as a separate platform.
Where can I verify the AI claims?
Use the documentation, benchmark pages, and linked repository materials to review implemented datasets, inference plumbing, distributed pipeline details, and roadmap milestones.