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 Mistral Small 22B Deployment Guide
Mistral Small 22B is the right model when 7B is not enough but 70B is overkill. Here is the deployment recipe on dedicated GPU hardware.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Eight AI Cost Optimization Techniques for Self-Hosted Inference
Concrete cost-reduction techniques for self-hosted AI workloads — from FP8 quantisation to prefix caching to multi-LoRA serving — with the…
RTX 5060 Ti 16 GB RAG Stack Install in Under an Hour
The complete install recipe for a working RAG stack on a freshly-provisioned RTX 5060 Ti server. Llama 3.1, BGE, Qdrant,…
RTX 5060 Ti 16 GB as a SaaS RAG Backend
Building a multi-tenant RAG backend on a single RTX 5060 Ti 16 GB. Sizing, isolation, cost-per-tenant, and the limit before…
Dedicated GPU Rental vs On-Prem Hardware Buyout: The ROI Math
Should you rent a dedicated GPU monthly or buy the hardware outright? Real ROI math across 1, 2, and 3…
AI Chatbot Streaming Architecture: From Browser to GPU and Back
End-to-end streaming chatbot architecture — browser to API gateway to vLLM and back, with the fragility points that bite in…
Private Cloud AI vs Public API: Architecture Decision Framework
When does a private AI cloud (dedicated GPUs in your VPC) beat hosted API access? The decision framework with real…
Self-Hosted LLM Fine-Tuning Pipeline: Data, Training, Eval, Deploy
End-to-end fine-tuning pipeline on dedicated GPU hardware — data prep, training run management, evaluation, and merging back for deployment.
Multimodal LLM Deployment Guide: Vision-Language Models on Self-Hosted GPUs
Llama 3.2 Vision, Qwen 2.5 VL, MiniCPM-V — the strongest open-weight vision-language models in 2026 and how to deploy them.
NVIDIA NIM vs vLLM: Which Inference Stack for Production?
NVIDIA NIM packages models as containerised microservices with TensorRT-LLM optimisation. vLLM is the open-source de-facto. When does each one win?
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