GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
AI Failure Mode Analysis
What can fail in production AI — the catalogue of failure modes, with detection and mitigation for each.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Streaming Response Handling
SSE streaming for LLM responses — client patterns, server config, error handling. The reference implementation.
Data Quality for RAG
RAG quality is bounded by source data quality. Cleaning + deduplication + structure extraction matters as much as embeddings.
Vector Search Tuning: HNSW Parameters
HNSW is the default vector index. Three parameters matter for production: M, ef_construction, ef_search. The trade-offs.
RAG Evaluation Best Practices
How to build a RAG eval set that actually catches regressions — representative queries, golden chunks, grading rubrics.
Database + Vector Store Hybrid Architecture
Combining traditional database (Postgres, MySQL) with vector store (Qdrant, pgvector) for AI applications. The architecture patterns.
Multi-Region AI Failover
Active-passive AI failover across regions — warm standby, traffic shifting, data sync. The cost-effective resilience pattern.
LLM Routing Rules
How to route LLM requests intelligently across multiple backends — cost, quality, latency, fallback. The pattern library.
Batch Inference Optimisation
Optimising batch inference workloads — daily processing, large-scale extraction, embedding ingest. Different patterns from real-time.
Self-Hosted vs Azure AI Foundry 2026
Azure AI Foundry (formerly Azure ML / OpenAI) vs self-hosted dedicated GPU — the 2026 comparison.
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