GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Self-Hosted LLM Evaluation Pipeline: Eval Harness, Custom Benchmarks, Regression Detection
How to evaluate open-weight LLMs on your specific workload — lm-evaluation-harness, custom test sets, and a CI pipeline that catches regressions.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
FP8 vs FP16 LLM Inference: Real Quality Comparison Across Five Models
Hardware FP8 on Blackwell promises 2× throughput at minimal quality cost. We measured the actual quality drop across five popular…
FLUX.1 vs Stable Diffusion 3.5: Which Open Image Model in 2026?
FLUX.1 dev and Stable Diffusion 3.5 Large are the two strongest open image models in 2026. Quality, speed, hardware, and…
Self-Hosted RAG Evaluation Pipeline: Recall, Precision, Answer Quality
How to measure if your RAG stack is actually working — retrieval recall, reranker precision, and end-to-end answer quality with…
GPU Server Lifecycle Management: Provisioning, Operating, Decommissioning
The full lifecycle of a dedicated GPU server — from initial provisioning through 1-3 years of operation to decommissioning. What…
Llama 3.1 70B vs Llama 3.3 70B: Worth the Upgrade?
Meta's Llama 3.3 70B is a text-only refresh of 3.1. Same hardware profile, better reasoning. Here is whether it is…
Multi-Server AI Inference Load Balancing: Patterns and Pitfalls
Once you outgrow a single GPU server, load balancing becomes the new problem. Round-robin? Sticky sessions? KV-cache aware? Here is…
Self-Hosted AI Image Generation API: Architecture and Cost Math
Building a production image-generation API on dedicated GPU hardware — ComfyUI as backend, FastAPI wrapper, queueing, and cost-per-image at scale.
Open-Source LLM Licensing in 2026: A Practical Comparison
Apache 2.0, Llama Community License, Cohere CC-BY-NC, Qwen License — what each one allows, what it blocks, and which models…
Voice Agent Latency Optimization: From 1.5s to Sub-500ms
Every component of a voice agent contributes 100-300ms. Here are the optimisations that take a 1.5s naive deployment to sub-500ms…
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