GPUs speed LLM parsing, OCR, and vision on large crawls. Most scrapers stay CPU-bound until models—not HTTP—become the throughput bottleneck you hit first.
GPUs speed LLM parsing, OCR, and vision on large crawls, but most scrapers stay CPU-bound until models become the bottleneck. These guides cover when a GPU actually helps a data pipeline.
The honest answer is that HTTP and parsing rarely need a GPU; model inference on the output sometimes does. Apify runs the crawl while GPU hardware handles heavy model stages. Below you will find guidance on GPU use in scraping pipelines.
Ollama and vLLM both let you run open-weight LLMs on your own hardware without sending prompts to OpenAI or Anthropic. But they are built for different points on the scale curve — Ollama for developers and small teams, vLLM for production multi-user inference workloads where throughput matters.
ChatGPT Team costs $25 per user per month. At 10 users that is $3,000 per year — before you factor in rate limits, data residency concerns, and the fact that every prompt you send trains future OpenAI models unless you opt out. Self-hosting an LLM stack eliminates per-seat costs, keeps prompts on hardware you control, and lets you run models tuned for your domain.
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docker-compose.yml: Ollama + LiteLLM + Open WebUI + Authentik + PostgreSQL + Redis + CaddyEvery prompt your team sends to ChatGPT Team or Copilot for Microsoft 365 leaves your network and is processed on OpenAI's or Microsoft's servers. For most teams that's an acceptable trade-off. For teams handling legal documents, source code, financial data, or customer PII, it is not. This guide deploys a private ChatGPT equivalent — Llama 3 70B running on your own GPU hardware, with a ChatGPT-quality web interface, your company's SSO, and per-team usage budgets — entirely inside your infrastructure.
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docker-compose.yml: Dify (API + worker + web + sandbox) + Qdrant + Ollama + PostgreSQL + Redis + CaddyRetrieval-Augmented Generation (RAG) lets you ask questions against a private document corpus and get answers grounded in the actual content. The dominant hosted pattern — OpenAI Assistants + Pinecone — works, but every document chunk and every query travels to OpenAI's servers. For legal contracts, internal knowledge bases, or any non-public data, that is a compliance liability.
This guide deploys a fully self-hosted RAG stack: Dify as the orchestration and document management layer, Qdrant as the vector database, and Ollama serving both the embedding model and the chat LLM — all on a single Liquid Web VPS.
Self-hosting OpenClaw with a cloud API backend is easy. But cloud APIs have costs that scale with usage, and they receive every message you send. If your team uses OpenClaw heavily, or if data privacy is a concern, local GPU inference solves both problems: your data stays on your hardware, and you pay a flat server rate instead of per-token fees.
ComfyUI is a node-based workflow editor for Stable Diffusion and FLUX. It is more powerful and flexible than AUTOMATIC1111, with a steeper learning curve. The catch: SDXL wants 12GB+ VRAM, and FLUX wants 24GB+. Most consumer GPUs fall short. The solution: run ComfyUI on a cloud GPU server and access it via SSH tunnel or reverse proxy. This guide covers setup on a fresh GPU server, model management, essential workflows, and remote access. For hosting, Liquid Web offers GPU VPS with RTX 4090, A100, and H100 options.
Fine-tuning adapts a pre-trained model to your domain — support tickets, legal docs, code style, or brand voice. QLoRA makes it practical on a single GPU: quantize the base model to 4-bit, train small LoRA adapters, then merge and deploy. This guide covers the full pipeline: dataset format, libraries, training script, saving and loading, evaluation, and cost estimates. For hardware, Liquid Web offers A100 and H100 GPU servers by the hour.
Deploy Meta's LLaMA 3 as a high-throughput, OpenAI-compatible inference API on a dedicated GPU server — with quantization, load testing, and Prometheus monitoring included.
If you've run LLaMA 3 locally with Ollama or llama.cpp, you've hit the ceiling: single-user latency is fine, but concurrency crumbles past three or four simultaneous requests. vLLM solves that. It uses PagedAttention and continuous batching to serve dozens of concurrent requests on the same GPU that would otherwise stall at one.
This guide walks through deploying LLaMA 3 (8B and 70B) on a dedicated GPU server using vLLM — covering Docker setup, model quantization, the OpenAI-compatible API, load testing with Locust, and Prometheus monitoring. All steps are tested on an NVIDIA A100 80 GB, which you can rent from Liquid Web.
Standard web scraping does not need GPUs. GPU demand arises in the post-processing stage: embedding text for RAG pipelines, running inference on scraped images, or fine-tuning models on collected training data. Separate the GPU-intensive processing from the scraping actor to optimize cost for each stage independently.
Apify handles the scraping and data storage; GPU inference runs on separate infrastructure—AWS EC2 GPU instances, RunPod, Vast.ai, or a local server. Apify webhooks trigger the GPU workload when scraped data is ready. This decoupled architecture lets you scale scraping and inference independently based on their different cost profiles.
RunPod and Vast.ai offer flexible spot pricing for batch inference jobs. AWS SageMaker and GCP Vertex AI provide managed inference endpoints with auto-scaling. Lambda Labs offers hourly GPU instances for training. Match instance type to your VRAM requirement: 24GB for most 7B models, 80GB for 70B quantized inference.
Batch text chunks into groups of 64-256, run through sentence-transformers or OpenAI embedding API, and write to a vector store. Local GPU embedding with ONNX Runtime is fast and cheap at scale; API embedding is simpler but costs per token. Typical throughput: 50,000 short text chunks per GPU-hour at 16-bit precision.
