GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Run Gemma 2 on a Dedicated GPU Server
Complete guide to deploying Google's Gemma 2 on a dedicated GPU server. Covers GPU recommendations for 2B, 9B, and 27B variants, vLLM setup, benchmarks, and optimisation tips.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Run Phi-3 on a Dedicated GPU Server
Complete guide to running Microsoft Phi-3 on a dedicated GPU server. Covers GPU selection for all Phi-3 sizes, vLLM and…
Run Qwen 2.5 on a Dedicated GPU Server
Complete guide to running Qwen 2.5 on a dedicated GPU server. Covers GPU selection, installation, API setup, performance benchmarks, and…
Run YOLOv8 on RTX 4060 (Object Detection Setup)
Step-by-step guide to running YOLOv8 object detection on an RTX 4060. Covers VRAM requirements, Ultralytics setup, inference benchmarks, and real-time…
LLaMA 3 vs Mistral 7B: Performance and Cost Compared
Head-to-head comparison of LLaMA 3 8B and Mistral 7B covering inference speed, quality benchmarks, VRAM usage, and hosting costs on…
Run Mixtral 8x7B on RTX 3090 (MoE Deployment)
Guide to deploying Mixtral 8x7B on an RTX 3090 with 24 GB VRAM. Covers VRAM constraints for MoE models, quantisation…
24GB vs 16GB vs 8GB VRAM: Which Do You Need for AI?
Not sure how much VRAM you need? This guide maps 8GB, 16GB, and 24GB tiers to specific AI models, workloads,…
RTX 3050 for AI: Budget GPU Capabilities
The RTX 3050's 6GB VRAM is the most budget-friendly option for AI. Here's exactly what you can and cannot run,…
RTX 5090 for AI: Is 32GB the New Standard?
The RTX 5090 combines 32GB GDDR7 with Blackwell architecture. Here's what it means for LLM inference, image generation, and AI…
RTX 5080 for AI: Blackwell Performance Guide
The RTX 5080 brings Blackwell architecture and GDDR7 memory to 16GB. Here's how it performs for AI inference, image generation,…
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