GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Caching Strategies for LLM Inference: Beyond Prefix Caching
Five caching layers that reduce LLM cost — prompt cache, response cache, embedding cache, retrieval cache, KV cache. When each one helps.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
AI Edge Deployment vs Centralised Self-Hosting
Edge devices (Jetson Orin, mini PCs) vs centralised dedicated GPU servers — when each one wins for AI inference workloads.
Multi-Region AI Inference Architecture: When and How
When does multi-region AI deployment pay back? Latency, compliance, and cost factors plus the architecture pattern that works.
Self-Hosted Text Classification: BERT, DeBERTa, and LLM-as-Classifier
Classification workloads — sentiment, intent, content moderation — on dedicated GPU. When to use BERT-class encoders vs LLM-as-classifier.
Self-Hosted DeepSeek R1 Deployment: Reasoning Model on Dedicated GPU
DeepSeek R1 is the open-weight reasoning model. Hardware sizing, deployment recipe, and what reasoning models actually buy you.
Designing an AI Inference SLA: What’s Realistic, What’s Not
What SLA targets are achievable for self-hosted AI inference? Realistic numbers for uptime, latency, and the architecture decisions that hit…
Self-Hosted AI Safety Guardrails: Llama Guard, Detoxify, Content Filtering
Adding safety guardrails to a self-hosted AI deployment — Llama Guard for prompt classification, Detoxify for output filtering, custom rules.
Open-Source vs Frontier Closed LLMs: When Each One Wins
Open-weight LLMs have caught up dramatically but frontier closed models still lead on hardest tasks. Here is the honest 2026…
Eight AI Self-Hosting Mistakes That Cost Real Money
Eight specific mistakes we see customers make on their first self-hosted AI deployment, with the fixes that recover the cost.
Version Pinning Strategy for AI Deployments: What to Pin, How Tight
AI stacks have many moving versions — driver, CUDA, vLLM, model commit. Pinning the wrong layer too tight breaks security;…
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