LlamaIndex Hosting
Run LlamaIndex RAG Pipelines on Dedicated UK GPU Servers
Build production-grade retrieval-augmented generation applications with LlamaIndex on bare metal. Full root access, no API fees, predictable monthly pricing.
What is LlamaIndex Hosting?
LlamaIndex is a developer-first framework for building retrieval-augmented generation (RAG) applications. It handles the full pipeline — data ingestion, document parsing, indexing, retrieval, and response synthesis — so you can connect your own documents, databases, and APIs to any large language model.
With GigaGPU’s dedicated GPU servers you get the hardware to run LlamaIndex alongside a local LLM such as LLaMA, Mistral, or DeepSeek. No per-token API fees, no shared resources, no data leaving your UK-based server. Deploy vector stores, embedding models, and query engines on a single machine with full root access.
LlamaIndex supports hybrid retrieval, reranking, graph indices, agent workflows, and production-grade evaluation — making it the go-to framework for teams building document Q&A, knowledge assistants, and enterprise search systems that need accuracy and control.
Trusted by AI teams, SaaS platforms, and research labs building RAG applications across the UK and Europe.
LlamaIndex RAG Pipeline — How It Works
LlamaIndex orchestrates every stage of retrieval-augmented generation, from document ingestion to answer synthesis — all running locally on your GPU server.
LlamaIndex Components You Can Self-Host
Key building blocks of the LlamaIndex ecosystem — all deployable on a dedicated GigaGPU server with full root access.
LlamaIndex is fully open source (MIT licence) — install via pip install llama-index on any GigaGPU server. Pair with Ollama or vLLM for local LLM inference.
Why Host LlamaIndex on a Dedicated GPU?
Running LlamaIndex locally with a self-hosted LLM eliminates per-token API costs, keeps your documents private, and gives you full control over every component of your RAG pipeline.
Cloud API Approach
Dedicated GPU Approach
LlamaIndex Hosting Use Cases
From private document search to production knowledge assistants — LlamaIndex on dedicated GPUs powers it all.
Document Q&A
Index PDFs, Word docs, and Markdown files with LlamaIndex’s VectorStoreIndex. Ask natural language questions and get cited answers grounded in your source material — all running privately on your GPU server.
Enterprise Knowledge Assistant
Connect LlamaIndex to Notion, Confluence, Slack, Google Drive, and databases via LlamaHub loaders. Give your team an AI assistant that searches across all internal knowledge with full data sovereignty.
Legal & Compliance Research
Parse complex legal documents with LlamaParse’s table and layout handling. Build retrieval pipelines that cite specific clauses and sections — critical for legal teams needing traceable AI answers.
Healthcare & Clinical Data
Keep patient data on-premises while using RAG to search clinical records, research papers, and treatment guidelines. No data leaves the UK-based server — simplifying GDPR and NHS IG compliance.
Financial Analysis & Due Diligence
Index earnings reports, SEC filings, and market data. LlamaIndex’s structured extraction and query routing lets analysts ask complex questions across thousands of financial documents instantly.
Academic & Research RAG
Build literature review assistants that index hundreds of papers and retrieve relevant passages with citations. Ideal for universities and research teams who need reproducible, private AI workflows.
Customer Support Automation
Deploy a RAG-powered support bot that retrieves answers from your helpdesk articles, product docs, and FAQs. LlamaIndex’s Chat Engine provides conversational memory for multi-turn support sessions.
Codebase Q&A & Documentation
Index your repositories and internal documentation. Developers can query codebases in natural language — useful for onboarding, debugging, and navigating large legacy projects with LlamaIndex agents.
LlamaIndex Hosting Pricing
Dedicated GPU servers for running LlamaIndex with a local LLM. Fixed monthly pricing — no per-query or per-token fees.
Token throughput figures are rough estimates under single-user, single-GPU conditions at Q4_K_M quantisation. Real-world performance varies with concurrent requests, context length, and configuration. See benchmark methodology →
GPU Performance Overview for LlamaIndex Workloads
A RAG stack splits VRAM between the LLM, embedding model, and KV cache. Here’s how each GPU handles a typical LlamaIndex deployment — including VRAM budgets and recommended stacks.
Enough VRAM for a 7B LLM at Q4 plus an embedding model. Ideal for building and testing LlamaIndex pipelines before scaling up.
via Ollama
The sweet spot for most LlamaIndex deployments. 24GB fits a 13B LLM at Q4 alongside embeddings and generous KV cache for long context retrieval.
via Ollama
Blackwell architecture delivers the fastest 7B inference on 16GB. Ideal for high-concurrency RAG APIs where response speed matters more than model size.
via Ollama
32GB RDNA 4 at a competitive price point. Fits 33B LLMs at Q4 alongside embeddings with VRAM to spare — a strong option for teams running larger models in RAG without the RTX 5090 premium.
via Ollama
Fastest single-GPU option. 32GB GDDR7 runs 13B at full Q8 quality or 70B at Q2 — with the Blackwell architecture delivering the highest token throughput available.
via Ollama
96GB enables full-quality 70B models at Q4 alongside large embedding models and extensive KV cache. The only single-GPU option for enterprise-grade RAG with the largest open source LLMs.
via Ollama
Full GPU Benchmark Comparison
Estimated throughput running LLaMA 3 8B at Q4_K_M via Ollama. Single user, single GPU. Higher is faster.
Estimates only · LLaMA 3 8B Q4_K_M · Single user · RAG latency also depends on embedding model, vector store, and retrieval strategy · Full benchmark methodology →
Deploy LlamaIndex in 4 Steps
From order to running RAG queries in under 30 minutes.
Choose Your GPU & Configure
Pick the GPU that fits your LLM size and RAG throughput needs. Select your OS (Ubuntu 22/24, Debian, Windows) and NVMe storage size.
Server Provisioned
Your dedicated GPU server is provisioned and you receive SSH or RDP credentials. Typical deployment time is under one hour.
Install LlamaIndex & Ollama
Run pip install llama-index and install Ollama for local LLM inference. Pull your chosen model and set up a vector store like ChromaDB or FAISS.
Index Documents & Query
Load your documents, build a VectorStoreIndex, and start querying. Expose as a FastAPI endpoint or connect to Open WebUI for a chat interface.
Compatible Tools & Integrations
LlamaIndex works alongside every major LLM framework and vector store — all installable on your GigaGPU server.
LlamaIndex Hosting — Frequently Asked Questions
Settings.llm at the local endpoint. No OpenAI API key required.Available on all servers
- 1Gbps Port
- NVMe Storage
- 128GB DDR4/DDR5
- Any OS
- 99.9% Uptime
- Root/Admin Access
Our dedicated GPU servers provide full hardware resources and a dedicated GPU card, ensuring unmatched performance and privacy. Perfect for self-hosting LlamaIndex RAG pipelines, knowledge assistants, document Q&A systems, and any other retrieval-augmented generation workload — with no shared resources and no token fees.
Get in Touch
Have questions about which GPU is right for your LlamaIndex workload? Our team can help you choose the right configuration for your RAG pipeline, model size, and throughput requirements.
Contact Sales →Or browse the knowledgebase for setup guides on LlamaIndex, Ollama, and more.
Start Hosting LlamaIndex Today
Flat monthly pricing. Full GPU resources. UK data centre. Build production RAG pipelines with LlamaIndex, Ollama, and any open source LLM.
