GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
AI Team Handoff Documentation
What documentation do you need so your AI deployment outlives the original engineer? The minimum viable handoff doc.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Graceful Error Handling for AI APIs
Production-grade error handling for LLM APIs — structured errors, retry semantics, user-friendly messages.
AI Cost Allocation by Feature
Attributing AI infrastructure cost to product features — for engineering decisions, not just finance reporting.
AI MLOps Stack in 2026
What does a modern MLOps stack look like for self-hosted AI in 2026? The components, the integrations, the gaps.
AI Output Watermarking and Provenance
Watermarking AI-generated content — statistical methods, content provenance (C2PA), the practical state in 2026.
AI Shadow Deployment Pattern
Shadow deployment for AI: send requests to new model alongside production; compare without affecting users. The right validation pattern.
Feedback Loops and RLHF Self-Hosted
Capturing user feedback into model improvement loops — thumbs / rating / explicit corrections feeding back into DPO training.
AI Data Pipeline: Batch vs Stream
Batch vs streaming for AI data pipelines — ingestion, embedding, indexing. When each fits.
Rate Limiting and Fairness for AI APIs
Rate limits for AI APIs — token-bucket, leaky-bucket, per-tenant fairness. The patterns and the gotchas.
Prompt Injection Defences
Defending production LLMs against prompt injection — instruction hierarchy, input sanitisation, output filtering, dual-LLM patterns.
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Browse ArticlesReady to deploy your AI workload?
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