GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
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.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
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…
RTX 5060 Ti 16 GB for YOLO Hosting: Concurrency and Multi-Stream Setup
Hosting multiple YOLO inference streams on a single RTX 5060 Ti — for security camera fleets, retail analytics, and multi-camera…
Upgrading from RTX 5060 Ti 16 GB to RTX 6000 Pro 96 GB
The 6000 Pro is 6× the VRAM and 6.5× the price of the 5060 Ti. When does that upgrade pay…
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…
RTX 4060 Ti vs RTX 5060 (Blackwell) for LLM Hosting: A Generation in Review
The RTX 5060 (8 GB Blackwell) replaced the RTX 4060 Ti as the entry-tier AI card. Here is how the…
Qwen-VL Vision-Language Benchmark on the RTX 5060 Ti 16 GB
Qwen 2.5 VL is the strongest open-weight vision-language model that fits 16 GB. Here is how it performs on a…
Fine-Tuning Throughput on the RTX 5060 Ti 16 GB: Tokens per Second by Method
How many fine-tuning tokens-per-second can a single RTX 5060 Ti 16 GB process? Real numbers across QLoRA, LoRA, and full…
LoRA Fine-Tuning on the RTX 5060 Ti 16 GB: Practical Walkthrough
LoRA fine-tuning on a single 5060 Ti — without QLoRA tricks. When LoRA beats QLoRA, what hyperparameters to use, and…
FLUX.1 Images per Second by GPU: Real Benchmarks Across Every Card We Host
Real images-per-minute throughput for FLUX.1 dev and schnell on every GPU we rent — FP16, FP8 and GGUF quantisation paths.
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