GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Upgrading from RTX 5060 Ti 16 GB to RTX 5090 32 GB: When It Pays Back
The 5090 is exactly 2x the 5060 Ti's price. The capability gap is wider than 2x for some workloads, narrower for others. Here is the upgrade decision framework.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Context Budget on the RTX 5060 Ti 16 GB: How Much Context Can You Afford?
Context length costs VRAM. On a 16 GB card, the trade-off between long context, model size, and concurrent users is…
8B LLM VRAM Requirements: Llama 3, Qwen, Phi-3 and the Rest
Exactly how much VRAM 8B-class language models need at FP16, FP8, and AWQ-INT4 — plus KV cache for long-context deployments.
Maximum LLM Size That Fits the RTX 5060 Ti 16 GB
How big can an LLM be and still fit on a 16 GB GPU? The precise model-size ceiling per quantisation…
Qwen 2.5 14B Benchmark on the RTX 5060 Ti 16 GB
Qwen 2.5 14B is too big for the 5060 Ti at FP16 but fits at AWQ-INT4. Real benchmarks for that…
Llama 3 8B Benchmark on the RTX 5060 Ti 16 GB
Real Llama 3.1 8B inference numbers on a single RTX 5060 Ti 16 GB across FP16, FP8 and AWQ-INT4 —…
Self-Hosted AI Infrastructure Patterns That Worked in 2026
A retrospective look at the patterns that actually shipped and survived in self-hosted AI infrastructure across our customer base in…
Enterprise AI Architecture Checklist for Self-Hosted Deployments
A pragmatic checklist for enterprise AI deployments — security, compliance, observability, cost control, and the operational pieces auditors will ask…
Self-Hosted Document Summarisation Pipeline on Dedicated GPU
Summarising long documents — reports, transcripts, contracts — on self-hosted infrastructure. The map-reduce pattern, hardware sizing, and the right model.
Open-Weight Model Release Cycle in 2026: What to Expect
How fast do open-weight LLMs get released and deprecated? Planning your deployment around the release cadence.
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