GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
IBM Granite Code 34B Self-Hosted
IBM's Granite Code 34B is an enterprise-oriented coding model with Apache 2.0 licence - the cleanest commercial licence in the coding LLM space.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
GLM-4 9B Chat Self-Hosted
Zhipu AI's GLM-4 9B is a compact model with strong tool-use and function-calling support - a practical alternative to Llama…
Gemma 2 27B on RTX 5090 – Complete Guide
Gemma 2 27B is the sweet spot in Google's open-weights lineup - stronger than the 9B, smaller than 70B class.…
Full Fine-Tune of a 7B Model on RTX 6000 Pro
When LoRA is not enough, full parameter fine-tuning of a 7B model fits comfortably on a 96GB RTX 6000 Pro.…
Flash Attention 2 Setup on a GPU Server
Flash Attention 2 is the default memory-efficient attention kernel in 2026. Getting it installed correctly on a dedicated GPU avoids…
Yi 34B on RTX 6000 Pro
01.ai's Yi 34B delivers strong bilingual performance and long context. On a 96GB card it runs at FP16 with serious…
vLLM Structured Output and Guided Decoding
Force the model to emit valid JSON, a regex, or a choice from a set. vLLM supports three backends with…
vLLM max-model-len and GPU Memory Utilisation Tradeoff
Two vLLM parameters jointly decide how much concurrency your dedicated GPU can sustain. Get them wrong and you leave half…
vLLM Engine Args Reference – What Each Flag Actually Does
A compressed reference to the vLLM engine flags that matter in production, grouped by what they actually affect.
Unsloth Fine-Tuning on RTX 4060 Ti 16GB
Unsloth's optimised kernels let you fine-tune 8B-class models on a single 16GB card with surprising throughput. Here is the setup.
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