Multi-Tenant GPU Serving: Batching, Caching and Cost Per Token
Cut GPU inference cost: why batching is nearly free, the KV cache as your capacity limit, prefix caching, prefill vs decode, MIG and adapters, cost per million tokens.
Read MoreAI & GPU Infrastructure
GPU servers, LLM inference and machine learning workloads on dedicated infrastructure. 17 articles from the MassiveGRID engineering teams.
Monitoring GPU Utilization: nvidia-smi, DCGM and Grafana
Monitor GPUs properly: the metrics that distinguish busy from starved from throttling, DCGM exporter with Prometheus, four alerts worth having, and reading the graphs.
Read MoreGPU Passthrough on Proxmox: The Six Things That Must Be True
Proxmox GPU passthrough: IOMMU groups, binding the card to vfio-pci, q35 and OVMF, the failures and what they mean, and why a passed-through VM cannot migrate.
Read MoreFine-Tuning an Open Model on a Single GPU Server
Fine-tune an LLM on one GPU: why LoRA and QLoRA fit, sequence length as the real limit, a working PEFT config, data quality, when to stop, and merge vs adapter.
Read MoreCPU-Only LLM Inference: What Actually Works Without a GPU
CPU LLM inference: why bandwidth not compute is the limit, which workloads run well, llama.cpp setup, quantisation, sizing and the honest GPU comparison.
Read MoreRenting a GPU Server vs Buying: The 3-Year Cost Model
GPU rent vs buy over three years: what a purchase really includes, published rental totals, the utilisation break-even, and when each option genuinely wins.
Read MoreSelf-Host Whisper for Speech-to-Text on a GPU Server
Self-host Whisper: why the implementation matters more than the model size, faster-whisper setup, VAD, batching, diarisation limits and whether you need a GPU.
Read MoreRun Stable Diffusion on a GPU Server: Setup and Sizing
Run Stable Diffusion on a GPU server: what consumes VRAM, ComfyUI as a service, the memory-saving flags, matching card to workload and licence risk.
Read MoreBuild a RAG Stack on Your Own Server: Postgres, pgvector and an LLM
Build a self-hosted RAG stack: pgvector setup, HNSW vs IVFFlat, chunking that decides quality, hybrid search, reranking, sizing and the real failure modes.
Read MoreGPU Server Hosting in Europe: Latency, Sovereignty and Cost
European GPU hosting compared: GDPR transfers and the CLOUD Act, where transatlantic latency actually hurts, egress costs, and choosing Frankfurt or London.
Read MoreA100 vs H100 vs RTX 6000 Ada: Which GPU for Which AI Workload
A100, H100 and RTX 6000 Ada compared for AI: memory bandwidth vs compute, FP8, NVLink, hourly vs monthly cost, and which card suits each workload.
Read MoreSelf-Host an LLM API with vLLM on a GPU Server
Serve an OpenAI-compatible LLM API with vLLM: continuous batching, PagedAttention, the flags that matter, systemd setup, nginx and throughput benchmarking.
Read MoreGPU Server Requirements for Local LLMs: VRAM by Model Size
How much VRAM a local LLM really needs: weight sizes by quantization, the KV cache formula, concurrency maths and which GPU fits which model.
Read MoreHow to Run Ollama on a VPS: Self-Hosted LLM Inference
Run Ollama on a VPS for self-hosted LLM inference: CPU vs GPU sizing, systemd tuning, nginx auth, the OpenAI-compatible API and when to move to vLLM.
Read MoreRunning AI and ML Workloads on a VPS: What You Need to Know
Running AI and ML Workloads on a VPS: Training vs Inference: Two Very Different Workloads and AI/ML Workloads That Run Well on a VPS.
Read MoreBest VPS for n8n AI Agents: Server Requirements for LLM-Powered Automation
Best VPS for n8n AI Agents: n8n as an AI Agent Platform, VPS Requirements for AI Workloads and Sizing Tiers: API-Only, Hybrid, and Local LLM.
Read MoreNVIDIA H100 vs H200: Key Differences in Performance, Memory & AI Power
NVIDIA H100 vs H200: The NVIDIA H100 and H200 are both high-performance GPUs based on NVIDIA’s Hopper architecture, designed for AI, high-performance.
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