GPU Hosting & AI Engineering Blog
Benchmarks, GPU comparisons, deployment guides, and cost analysis — everything you need to run AI on dedicated GPU servers.
GPU Power Management: Persistence Mode
Configure GPU power management and persistence mode for AI servers. Covers nvidia-persistenced, power limits, clock speeds, power draw monitoring, and maximizing GPU efficiency on dedicated servers.
Latest Articles
Fresh benchmarks, comparisons, and deployment guides from the GigaGPU team.
Memory Bandwidth vs TFLOPS: Why It Matters
Understand why memory bandwidth matters more than TFLOPS for LLM inference. Covers the memory wall, roofline analysis, bandwidth-bound vs compute-bound…
Auto GPU Recovery After Crashes
Automate GPU server recovery after crashes. Covers systemd restart policies, GPU reset procedures, watchdog scripts, health checks, OOM recovery, and…
Tensor Cores Explained
Understand NVIDIA Tensor Cores and how they accelerate AI workloads. Covers matrix multiply acceleration, precision modes, generation differences, how to…
GDDR6 vs GDDR6X vs GDDR7 for AI
Compare GDDR6, GDDR6X, GDDR7, and HBM memory technologies for AI workloads. Covers bandwidth, power efficiency, which GPUs use which memory,…
PCIe Gen4 vs Gen5 for AI
Compare PCIe Gen4 vs Gen5 for AI inference and training. Covers bandwidth differences, GPU-to-CPU transfer bottlenecks, NVLink comparison, multi-GPU scaling,…
FP16 vs BF16 vs FP8 for AI Inference
Compare FP16, BF16, and FP8 precision formats for AI inference. Covers numerical ranges, accuracy tradeoffs, throughput differences, GPU support, and…
CUDA Toolkit Install Fails on Ubuntu: Fix Guide
Fix CUDA toolkit installation failures on Ubuntu GPU servers. Covers dependency conflicts, broken package managers, kernel header issues, and clean…
CPU Bottleneck in AI: Detect & Fix
Detect and fix CPU bottlenecks in AI inference. Covers tokenization overhead, preprocessing stalls, CPU profiling, kernel optimization, NUMA binding, and…
GPU Utilization Below 50%: Diagnosis & Fix
Diagnose and fix GPU utilization below 50% on AI inference servers. Covers identifying bottlenecks, data pipeline stalls, batch size issues,…
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