GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
vLLM Deployment on the RTX 5090 32 GB: The Production Config
The vLLM launch flags that exploit Blackwell properly on a 5090 — FP8 weights, FP8 KV cache, prefix caching, optional speculative decoding.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
vLLM Deployment on the RTX 3090 24 GB: Production Recipe
The vLLM launch flags that work on Ampere — no FP8 hardware path, but 24 GB VRAM lets you run…
Qwen 2.5 32B Self-Hosted Deployment Guide
Qwen 2.5 32B sits in the awkward 32B middle — too big for a 32 GB card at FP16, too…
Llama 3 70B INT4 VRAM Requirements: The Precise Math
Llama 3 70B at AWQ-INT4 — exactly how much VRAM, with KV cache by context length, and which GPU configurations…
ComfyUI on the RTX 5060 Ti 16 GB: A Practical Setup Guide for SDXL, FLUX.1 and Beyond
How to deploy ComfyUI on a dedicated RTX 5060 Ti 16 GB server, with realistic memory budgets for SDXL, FLUX.1…
AI Vendor Lock-In: How to Mitigate It in 2026
OpenAI / Anthropic lock-in is real. Open-weight models + standard APIs eliminate most of it. Here is the practical playbook.
Qwen 2.5 Coder 14B on the RTX 5060 Ti 16 GB
Qwen 2.5 Coder 14B is one of the strongest open-weight code models. On a 5060 Ti it needs INT4 to…
Phi-3 Mini Benchmark on the RTX 5060 Ti 16 GB
Phi-3 Mini (3.8B) is small enough that the 5060 Ti is dramatic overkill. Real benchmarks for high-throughput Phi-3 deployments.
Self-Hosted AI Deployment: The Master Checklist
A consolidated checklist of everything you should verify before launching a self-hosted AI inference deployment to production.
RTX 5060 Ti 16 GB vs A100 40 GB for LLM Inference
Consumer Blackwell vs older datacenter Ampere. The 5060 Ti is much cheaper but A100 40 GB has unique strengths. Head-to-head.
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