GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
CUDA Graphs in vLLM
CUDA Graphs eliminate kernel launch overhead in vLLM's decode loop. ~10-20% throughput win on small-batch inference.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Prefill / Decode Disaggregation
Splitting prefill and decode onto different GPUs — emerging pattern for high-throughput LLM serving at scale.
Mixture-of-Experts (MoE) Deployment
Deploying MoE models (Mixtral, DeepSeek V3) in production — specific tuning, expert routing, memory considerations.
FlashAttention-3 Impact
FlashAttention-3 (2024) brings ~1.5-2× throughput improvement over FA-2 on Hopper / Blackwell. Real numbers and what changed.
Knowledge Distillation Self-Hosted
Distil a 70B model into a 7B for production — the pattern that keeps quality close while cutting cost ~10×.
Quantisation-Aware Fine-Tuning
QAT (quantisation-aware training) for LLMs — train with simulated low-precision so the deployed quantised model holds quality.
AI Platform Engineering as a Discipline
AI platform engineering is becoming its own discipline in 2026 — what skills it requires and how it differs from…
AI Feature Deprecation Pattern
Sunsetting an AI feature gracefully — user communication, data preservation, replacement migration.
Self-Hosted AI Time to Value
How quickly does a self-hosted AI deployment deliver value? The realistic timeline from decision to production benefit.
AI Disaster Recovery Plan
Disaster recovery for self-hosted AI — data, models, configs, infrastructure. RTO / RPO targets and how to hit them.
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