--- author: "Umesh Malik" canonical: "https://umesh-malik.com/blog/tag/gpu" description: "Explore articles tagged with GPU by Umesh Malik — AI Engineer, LLM & GenAI Developer. Learn GPU best practices, practical tips, and in-depth guides." title: "Umesh Malik's Blog - GPU Articles | GPU Tutorials" tokens: 426 generator: "scripts/generate-page-markdown.mjs" --- [← Back to Blog](https://umesh-malik.com/blog) # GPU 3 articles [![Cover showing the PCIe bottleneck in traditional GPU-NIC architecture versus MTIA 300's built-in NIC design that delivers 1.2 TB/s without CPU mediation](https://umesh-malik.com/blog/eliminate-pcie-bottleneck-ai-training-cover.png) AI Engineering • Aug 25, 2026 ### Fix the PCIe Bottleneck in AI Training: How Built-in NICs Work Fix the PCIe bottleneck in AI training with built-in NICs. Meta's MTIA 300 reclaims 1.2 TB/s by eliminating host CPU mediation. 9 min read Read more →](https://umesh-malik.com/blog/eliminate-pcie-bottleneck-ai-training) [![Qwen3.8 27B VRAM budget: FP8 weights plus KV cache at 262K context on a single GPU](https://umesh-malik.com/blog/qwen3-8-27b-vram-kv-cache-math-cover.png) LLM Engineering • Aug 15, 2026 ### Qwen3.8 27B VRAM: how to fit 262K context in 16 GiB, not 64 Qwen3.8 27B VRAM math: 25.9 GiB of FP8 weights plus 16 GiB of KV cache at 262,144 tokens, not 64. The arithmetic, and where a 48 GB card breaks. 9 min read Read more →](https://umesh-malik.com/blog/qwen3-8-27b-vram-kv-cache-math) [![Cover showing the vLLM VRAM budget split into model weights, runtime overhead and KV cache, with the KV cache block math that converts free VRAM into concurrent sequences](https://umesh-malik.com/blog/vllm-throughput-tuning-flags-cover.png) LLM Engineering • Aug 8, 2026 ### vLLM throughput tuning: configure these four flags, not a bigger GPU vLLM throughput tuning starts with KV cache blocks, not a bigger GPU. The four flags that decide your tokens/sec, and the one that quietly backfires. 10 min read Read more →](https://umesh-malik.com/blog/vllm-throughput-tuning-flags)