GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Scaling Inference Horizontally vs Vertically
Bigger card or more cards - the oldest infrastructure question, applied specifically to LLM inference in 2026.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Ryzen AI Max+ 395 vs RTX 6000 Pro – Unified Memory Tradeoffs
96GB unified memory APU versus 96GB dedicated VRAM workstation GPU - when does the unified architecture actually win?
RTX 6000 Pro vs Pair of RTX 3090s – Throughput Comparison
Single 96GB workstation card or two 24GB Ampere cards combined - which delivers more tokens per dollar?
AI for SaaS: GPU Server Architecture Guide
How SaaS companies should architect GPU server infrastructure for AI features, covering model selection, multi-tenancy, scaling strategies, and cost planning.
AI for Agencies: Multi-Client GPU Hosting Setup
How agencies can set up multi-client AI hosting on dedicated GPU servers, covering isolation, model management, billing strategies, and GPU…
AI for Content Creation: Image + Text on One GPU
How to run both image generation and text generation models on a single GPU server for content creation workflows, with…
1 GPU vs 2 GPU: When Does Multi-GPU Make Sense?
Understand when scaling from one GPU to two GPUs makes sense for AI inference, including throughput gains, latency trade-offs, and…
Fine-Tuning VRAM Calculator: How Much Do You Need?
A practical VRAM calculator for LLM fine-tuning covering LoRA, QLoRA, and full fine-tuning across model sizes from 7B to 70B,…
How Long Does Fine-Tuning Take by GPU?
Fine-tuning time benchmarks for LLM training across eight GPUs, covering 7B to 70B models with LoRA and QLoRA, including cost-per-experiment…
Fine-Tune Mistral 7B: Hardware Requirements & Cost
Complete guide to fine-tuning Mistral 7B including VRAM requirements, GPU recommendations, training time benchmarks, and cost analysis for LoRA and…
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