GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
FastAPI AI Inference Server: Complete Build
Complete guide to building a FastAPI AI inference server on a dedicated GPU covering request validation, streaming, rate limiting, authentication, health checks, and production deployment.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Redis Queue for AI: Async Processing
Complete guide to building async AI inference processing with Redis queues covering job submission, GPU worker design, priority queues, result…
gRPC for AI Inference: High-Performance API
Complete guide to building a gRPC AI inference service on GPU servers covering protobuf definitions, server-side streaming, bidirectional communication, load…
Qwen 2.5 vs Qwen 2: Self-Hosting Upgrade Guide
Comparison of Qwen 2.5 and Qwen 2 covering architectural improvements, benchmark gains, VRAM impact, and step-by-step migration guidance for self-hosted…
Prometheus + Grafana: GPU Monitoring
Complete guide to monitoring GPU servers with Prometheus and Grafana covering DCGM exporter, custom inference metrics, alerting, dashboard design, and…
Kubernetes for AI: GPU Pod Config
Complete guide to configuring Kubernetes GPU pods for AI inference covering NVIDIA device plugin, resource requests, node affinity, autoscaling, and…
Celery + GPU: Distributed AI Tasks
Complete guide to running distributed AI tasks with Celery on GPU servers covering task routing, GPU worker configuration, result backends,…
Phi-3.5 vs Phi-3: What Microsoft Improved
Technical comparison of Phi-3.5 and Phi-3 covering the new MoE variant, multilingual expansion, benchmark improvements, and what changes for GPU…
Model Versioning on GPU Servers
Practical guide to implementing model versioning for AI inference on GPU servers covering storage strategies, metadata tracking, version switching, A/B…
CI/CD for AI Models: Automated Pipeline
Step-by-step guide to building CI/CD pipelines for AI model deployment covering automated testing, model validation, Docker image builds, rolling updates,…
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