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Llama 3: guides & tutorials

Llama 3 on GPU handles fast text tasks on scraped data—8B or 70B quants work well; pipe Apify JSON into batch classification or RAG prep.

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Llama 3 handles fast text tasks on scraped data, with 8B and 70B quants that run well on local GPUs. These guides cover piping Apify JSON into Llama 3 for batch classification or RAG prep.

Open weights mean you can process sensitive data locally without per-token cloud costs. Apify exports feed the model directly. Below you will find tutorials for using Llama 3 on web data.

Related topics

GPU7 min read

Fine-Tune LLaMA 3 with QLoRA on a Rented GPU: Complete Guide (2026)

· 7 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

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.

GPU15 min read

Serve LLaMA 3 with vLLM: Production Inference API on a GPU Server (2026)

· 15 min read
Yassine El Haddad
Yassine El Haddad
Software Developer & Automation Specialist

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.

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Frequently asked questions

Frequently Asked Questions

Llama 3 is Meta's open-weight large language model family, offering 8B and 70B parameter variants with strong reasoning and instruction-following capabilities. It can be run locally with Ollama, on GPU servers with vLLM, or via hosted APIs. It is popular for scraping-adjacent tasks like extraction, classification, and summarization where cost or privacy matters.

Fetch and clean page text, then prompt Llama 3 with a JSON extraction schema and example. Use structured output modes (JSON grammar constraints in Ollama or vLLM) to guarantee parseable responses. Batch requests for efficiency. Llama 3 70B matches GPT-4 quality on many extraction tasks at a fraction of the API cost.

The 8B model runs on a 12GB VRAM GPU (RTX 3080/4070) or Apple M2/M3 with 16GB unified memory at 4-bit quantization. The 70B model needs 48GB VRAM or multiple consumer GPUs. For CPU inference via llama.cpp, 32GB RAM handles the 8B model at modest throughput—useful for batch overnight jobs.

Yes, Llama 3 has improved multilingual capabilities compared to Llama 2, handling major European and Asian languages reasonably well. For production multilingual extraction, benchmark against your specific languages and domains. Specialized multilingual models like Qwen or Mistral multilingual variants may outperform Llama 3 on non-English corpora.