GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
QLoRA Fine-Tune on RTX 5060 Ti 16GB – Complete Guide
QLoRA with bitsandbytes NF4 lets you fine-tune up to 14 B parameters on a 16 GB card - code, config and timing.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
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…
Docker vs Bare-Metal for AI Inference: When the Container Tax Matters
Docker is convenient. For GPU AI workloads it sometimes leaves performance on the table. Here is when bare-metal wins and…
Self-Host an LLM: A Practical Guide From Hardware to Production
The end-to-end guide to self-hosting an open-weight LLM — pick the GPU, install vLLM, configure auth, monitor, and ship. The…
Monitoring GPU Usage on a Dedicated Server: Tools, Metrics, and Alerts
How to monitor GPU usage on a dedicated AI inference server — nvidia-smi, DCGM exporter, vLLM metrics, and the alerts…
Cheapest GPU for AI Inference in 2026: Five Tiers Compared
What is the cheapest GPU you can rent that actually runs production AI inference? Five tiers — from £69/mo to…
RTX 4090 24 GB vs RTX 5090 32 GB: The Generational Step
The RTX 5090 is the natural successor to the 4090. 33% more VRAM, 78% more bandwidth, native FP8. Here is…
vLLM Setup on the RTX 4090 24 GB: The Production Config
The vLLM launch flags that actually matter on a 24 GB Ada Lovelace card. Tuned for the workloads the 4090…
RTX 5060 Ti 16 GB for NLLB-200: Translation Throughput Across 200 Languages
Meta's NLLB-200 is the strongest open-weight translation model — 200 languages, dedicated to translation. The 5060 Ti hosts it at…
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…
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