GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Red-Teaming a Self-Hosted LLM
Adversarial testing for production LLM deployments. Prompt injection, data leakage, jailbreaks, output manipulation.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Capacity Planning for AI Inference
Capacity planning for self-hosted LLM inference — concurrent users, peak load, headroom, scaling triggers.
Cost Monitoring for Self-Hosted AI
Track £/M tokens, cache hit rate, fallback rate, and other cost-relevant metrics for self-hosted AI. The dashboard you actually need.
AI On-Call Rotation
On-call practices for production AI — what alerts to wake people for, how to rotate, what runbooks to write.
Multi-Region AI Deployment Patterns
Deploying AI across UK / EU / US regions for latency, residency, redundancy. The patterns that work and the ones…
Semantic Cache Implementation
Semantic caching for LLM responses — embed the query, look up similar past queries, return cached response. ~20-40% hit rate…
Eval Harness Design for LLM Production
What goes into a production eval harness — representative prompts, grading rubrics, automation, gating. The reference design.
Self-Hosted AI Resilience Patterns
Resilience patterns for self-hosted AI — redundancy, fallback, graceful degradation. Production-grade reliability.
LiteLLM Router for Production AI
LiteLLM as the routing layer between your application and multiple AI backends — self-hosted, hosted, fallback, retry.
Blue-Green Deployment for AI Services
Zero-downtime deploys for vLLM and AI services using the blue-green pattern. Specific gotchas for stateful inference.
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