jamesob
Featured
local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
Why it is worth attention
It goes beyond simple OCR API calls to deliver a complete, production-ready system with advanced features like multi-token prediction, zero-copy RAM handoff, KEDA-based autoscaling, and enterprise security controls.
Who it is for
- ML/AI Engineers
- Platform Engineers
- Engineers wanting to run OCR in production
- Developers interested in GPU-accelerated pipelines on Kubernetes
Use cases
- Building a self-scaling OCR system that handles charts, tables, and handwritten documents
- Deploying an event-driven document understanding pipeline with low-latency AI agent integration via MCP
- Enterprise document processing with internal load balancers and API gateway security
- Real-time OCR serving at 1.86 pages/second using continuous batching and GPU autoscaling
Strengths
- Production-grade architecture with separate T4 and A100 node pools and KEDA metric-driven autoscaling
- High throughput achieved via vLLM with Multi-Token Prediction (~50% increase) and chunked prefill
- Zero-copy document handoff using /dev/shm eliminates disk I/O bottlenecks
- Comprehensive security: internal load balancer, enterprise API gateway, JWT auth, and rate limiting
Considerations
- Requires access to cloud GPU instances (T4 and A100) on AKS or GKE, which may incur significant costs
- Course is based on a 6-week cohort; the repository is open-source but full setup may require significant time and expertise
- Dependence on specific SDKs (GLM-OCR) and model (Qwen 3.5) may limit customization without deep domain knowledge
README quick start
Getting Started
Description
Build, deploy, and scale a production-grade OCR pipeline using Rust, vLLM, Redis, KEDA, and Kubernetes.
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