Open Source LLM Hosting
Deploy Any Open Source Language Model on Dedicated UK GPU Servers
Run DeepSeek, LLaMA, Mistral, Qwen and more on bare metal GPU servers. Full root access, no token limits, predictable monthly pricing.
What is Open Source LLM Hosting?
Open source LLM hosting means running large language models — such as Meta’s LLaMA, DeepSeek, Mistral, or Qwen — on your own dedicated GPU server instead of paying per-token fees to a third-party API provider.
With GigaGPU’s dedicated GPU servers you get the full GPU card, NVMe-backed storage, and a UK-based bare metal environment. Deploy via Ollama, vLLM, LM Studio, or any framework in minutes. No shared resources, no usage caps, no data leaving your environment.
The landscape of open source LLM hosting has matured significantly — models like DeepSeek-R1 and LLaMA 3 have demonstrated competitive benchmark performance against many closed-source offerings, making self-hosted deployments a credible option for a wide range of production workloads.
Deployed by AI startups, SaaS platforms, and research teams across the UK and Europe.
Supported Open Source LLMs
Most popular open source LLMs supported by Ollama, vLLM, and Hugging Face Transformers can be deployed, depending on GPU memory and configuration.
Most popular open-weight models supported by Ollama, vLLM, Hugging Face Transformers, or llama.cpp are deployable. Compatibility depends on VRAM, quantisation, and framework support.
Best GPUs for Open Source LLM Hosting
Recommended configurations based on typical workloads.
The sweet spot for most LLM hosting needs. 24GB fits 13B models at full precision or 33B at Q4, with strong throughput and excellent price-to-performance.
Blackwell 2.0 architecture delivers the highest single-GPU throughput available for production chatbots, APIs, and multi-user inference at 70B model sizes.
96GB of GDDR7 VRAM enables 70B models at full Q4 quality and 405B at Q2. Ideal for enterprise deployments, RAG with large context windows, and fine-tuning runs.
RDNA 4 architecture with 32GB and 644 GB/s bandwidth — an excellent AMD alternative for teams using ROCm workflows or needing a high-VRAM option at a competitive price.
Which GPU Do I Need?
Answer three quick questions and we’ll recommend the right server for your LLM workload.
Open Source LLM Hosting Pricing
Token throughput figures are rough estimates under single-user, single-GPU conditions at Q4_K_M quantisation. Real-world performance varies significantly with concurrent requests, context length, cooling, and configuration. See benchmark methodology →
How Much Can You Save vs API Providers?
For high-volume workloads, a flat-rate dedicated GPU server is often significantly cheaper than paying per token. Here's how the models compare.
API Pricing
Dedicated GPU
Example: Production Chatbot at 10M Tokens/Day
API cost estimates are based on publicly listed per-token pricing at time of writing and are indicative only. Actual savings depend on model choice, usage patterns, and the specific API tier used. GPU server prices retrieved live from the GigaGPU portal. Use our full GPU vs API cost calculator →
Cost per 1M Tokens vs OpenAI — Calculator
Estimate your monthly cost savings when switching from API pricing to a dedicated GPU server.
Open Source LLM Hosting Benchmark — GPU Comparison
Estimated LLaMA 3 8B tokens/sec at Q4_K_M quantisation via Ollama. See our full benchmark page for detailed methodology.
| GPU | VRAM | LLaMA 3 8B tok/s | Max Model (Q4) | Relative Performance |
|---|---|---|---|---|
| RTX 3050 6GB | 6 GB | ~18 tok/s | ~5B | |
| RTX 4060 8GB | 8 GB | ~52 tok/s | ~7B | |
| RTX 4060 Ti 16GB | 16 GB | ~68 tok/s | 13B | |
| RTX 3090 24GB | 24 GB | ~85 tok/s | 33B | |
| RX 9070 XT 16GB | 16 GB | ~95 tok/s | 13B | |
| Radeon AI Pro R9700 | 32 GB | ~110 tok/s | 70B Q2 | |
| RTX 5080 16GB | 16 GB | ~140 tok/s | 13B | |
| RTX 6000 PRO 96GB | 96 GB | ~160 tok/s (70B) | 405B Q2 | |
| RTX 5090 32GB | 32 GB | ~220 tok/s | 70B Q2 |
Figures are estimates based on single-GPU, single-user inference at Q4_K_M quantisation using Ollama. Real-world throughput varies with concurrent users, context length, system RAM, and cooling. See full benchmark methodology →
Tokens Per Second by GPU — Visual Chart
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 · Full benchmark methodology →
Open Source LLM Hosting Use Cases
From internal tools to high-throughput production APIs — our dedicated GPU servers fit every workload.
AI Chatbot Hosting
Run a private ChatGPT-like chatbot on your own server. Deploy Open WebUI or a custom interface in front of most open-weight models — no usage caps, no data sharing.
OpenAI-Compatible API
vLLM and Ollama expose a drop-in OpenAI-compatible REST API. Point any existing integration at your server — zero code changes required.
RAG & LangChain Pipelines
Build retrieval-augmented generation pipelines with LangChain or LlamaIndex. Combine a local LLM with ChromaDB or Qdrant for private document Q&A.
AI Coding Assistants
Self-host CodeLlama, DeepSeek-Coder, or Qwen-Coder as a private coding assistant. Integrate with VS Code Continue or any IDE plugin.
Enterprise Private AI
Keep sensitive data on-premises. No data leaves your server — ideal for legal, healthcare, and financial sectors with compliance requirements.
Voice AI Agents
Combine a hosted LLM with Whisper ASR and Kokoro TTS for a fully self-hosted voice agent pipeline — no third-party API latency or per-call costs.
Multilingual AI
Deploy Qwen3, Mistral, or Gemma to serve customers in multiple languages. Open source models support 30+ languages natively.
Fine-Tuning & Research
Full GPU access for LoRA or QLoRA fine-tuning. Perfect for researchers, academics, and teams building custom model variants with Axolotl or Unsloth.
Compatible Frameworks & Platforms
Every GigaGPU server ships with full root access — install any open source LLM framework in minutes.
Deploy an Open Source LLM in 4 Steps
From order to running inference in under 30 minutes.
Choose Your GPU & Configure
Pick the GPU that fits your model size and 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 Ollama or vLLM
Run curl -fsSL https://ollama.com/install.sh | sh and pull supported models from Hugging Face or the Ollama library. Most popular open-weight models are available within seconds.
Start Serving Inference
Point your app at the local API endpoint or expose it via Nginx. You're live — unlimited tokens, zero per-call fees, forever.
Open Source LLM Hosting — Frequently Asked Questions
Everything you need to know about self-hosting open source language models on dedicated GPU hardware.
/v1/chat/completions). You can point any existing OpenAI SDK or integration at your server's IP address and it will work without code changes, making migration from closed-source APIs straightforward.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 open source LLMs, RAG pipelines, AI agents, coding assistants, and any other AI or deep learning workload — with no shared resources and no token fees.
Get in Touch
Have questions about which GPU is right for your LLM workload? Our team can help you choose the right configuration for your model size, throughput requirements, and budget.
Contact Sales →Or browse the knowledgebase for setup guides on Ollama, vLLM, and more.
Start Hosting Your Open Source LLM Today
Flat monthly pricing. Full GPU resources. UK data centre. Deploy LLaMA, DeepSeek, Mistral and more in under an hour.
