Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Feature flagging for AI features — rolling out new prompts, models, retrieval changes safely. Patterns that work.
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
When and how to retire an old model from production. Sunset timeline, migration support, eval bridging.
When your self-hosted AI breaks in production — the runbook for diagnosis, mitigation, and recovery.
How to attribute self-hosted AI cost to tenants / customers / departments. Per-token cost models that work in practice.
When to re-embed your corpus with a new embedding model — drift detection, quality benchmarks, cost.
Single-tenant dedicated GPU vs multi-tenant cloud — what changes in your security trust model.
Backup and disaster recovery for production vector stores. Qdrant / Weaviate / pgvector specifics.
How to roll out a new model version (Llama 3.1 → 3.3, or your fine-tune v2) safely. The blue-green pattern…
Production prompt management — version control, A/B testing, rollout patterns. Treat prompts like code.
How to measure ROI on a self-hosted AI deployment honestly — direct cost saving, productivity gains, the hidden costs.
Find exactly what you need — from GPU benchmarks to deployment tutorials.
AI Hosting & Infrastructure
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Browse ArticlesDedicated GPU servers from our UK datacenter. NVMe storage, 1Gbps networking, full root access.