GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
Tokens per Watt: Energy Efficiency
Benchmarking AI inference energy efficiency across GPU models measured in tokens per watt. Comparing power consumption against throughput to find the most cost-effective GPU for sustainable AI hosting.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
RAG Pipeline End-to-End Latency by GPU
Benchmarking complete RAG pipeline latency from query to response across GPU models. Measuring embedding, retrieval, reranking, and generation stages to…
PCIe Bandwidth: Multi-GPU Impact
Benchmarking PCIe bandwidth impact on multi-GPU LLM inference. Comparing NVLink, PCIe Gen 5, and PCIe Gen 4 interconnects for tensor…
NVMe vs SATA: Model Loading Speed
Benchmarking NVMe versus SATA SSD for LLM model loading times. Sequential read speeds, cold start differences, and storage recommendations for…
Thermal Throttling Impact on AI
Measuring how thermal throttling degrades GPU performance during sustained AI inference. Temperature thresholds, throughput loss, and cooling strategies for maintaining…
Quantized vs Full Precision: Quality Loss
Measuring actual quality loss from INT4 and INT8 quantisation compared to FP16 across reasoning, coding, and creative writing benchmarks. Data-driven…
First Token vs Streaming Throughput
Benchmarking time-to-first-token and streaming throughput across GPU models and LLM sizes. Understanding the two metrics that define perceived speed in…
GPU Memory During Inference by Model
Measuring actual GPU memory utilisation during LLM inference across model sizes, precision levels, and concurrent user counts. VRAM breakdown between…
Batch Inference: Size 1 to 128
Benchmarking LLM inference throughput from batch size 1 to 128. GPU utilisation, throughput scaling, and the diminishing returns curve for…
Context Scaling: 4K to 32K Performance
Benchmarking LLM inference performance as context windows scale from 4K to 32K tokens. Prefill latency, generation throughput, and VRAM consumption…
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