GigaGPU Blog

GPU Hosting & AI Engineering Blog

Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.

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Benchmarks

Batch Size Tuning for Max Throughput

Tune batch sizes for maximum GPU throughput in AI inference and training. Covers the latency-throughput tradeoff, continuous batching, VRAM limits, finding optimal batch size, and benchmarking methodology.

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Latest Articles

Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.

Benchmarks Apr 2026

Network Latency in AI Serving: Fix

Diagnose and fix network latency in AI serving pipelines. Covers TCP tuning, connection pooling, HTTP/2, gRPC, geographic placement, streaming optimization,…

Disk I/O Bottleneck: When Storage Slows GPU

Diagnose and fix disk I/O bottlenecks on GPU servers. Covers model loading delays, NVMe optimization, RAM caching, mmap loading, training…

GPU Profiling with nvidia-smi & Nsight

Profile GPU workloads with nvidia-smi and Nsight tools. Covers utilization monitoring, kernel-level profiling, memory analysis, bottleneck identification, and actionable optimization…

Mixed Precision Training Guide

Implement mixed precision training for faster AI model training on GPU servers. Covers AMP, loss scaling, BF16 vs FP16, common…

Memory-Mapped Model Loading

Use memory-mapped file loading to accelerate AI model startup. Covers mmap mechanics, safetensors mmap, reducing load times, lazy loading, shared…

CUDA Graph Optimization for Inference

Use CUDA Graphs to accelerate AI inference by eliminating kernel launch overhead. Covers graph capture, replay, vLLM integration, limitations, benchmarking,…

LLaMA 3.1 vs LLaMA 3: What Changed for GPU Hosting

Detailed comparison of LLaMA 3.1 and LLaMA 3 covering architecture changes, benchmark improvements, VRAM requirements, and what the upgrade means…

DeepSeek Coder vs DeepSeek Chat: Choosing the Right Variant

Comparison of DeepSeek Coder and DeepSeek Chat variants covering training differences, benchmark performance on code vs conversation tasks, and deployment…