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AI Coding Assistant Hosting

Self-Host Your Own AI Coding Assistant on Dedicated UK GPU Servers

Build a private, self-hosted alternative to GitHub Copilot. Run open-source coding models with Continue, Cline, Aider, or Roo Code on your own GPU server — unlimited completions, fixed monthly pricing, and your source code never leaves your infrastructure.

What is AI Coding Assistant Hosting?

AI coding assistant hosting means running your own private alternative to GitHub Copilot, Cursor, or Codeium on a dedicated GPU server. Instead of paying per-seat or per-token fees to a third-party service, you deploy an open-source code model and connect it to your IDE via tools like Continue, Cline, Aider, or Roo Code.

With GigaGPU you get a full dedicated GPU card, NVMe storage, and a UK-based bare metal environment. Deploy your coding model via vLLM or Ollama, expose an OpenAI-compatible API, and point your IDE extension at it — real-time code completions, inline chat, and multi-file edits powered by your own private infrastructure.

Self-hosted coding assistants are ideal for teams that need source code privacy, want to eliminate per-seat licensing costs, require custom model selection, or need to embed AI coding features into internal tools and SaaS products without third-party dependencies.

11+
GPU Models Available
UK
Data Centre Location
99.9%
Uptime SLA
Any OS
Full Root Access
1 Gbps
Port Speed
No Limits
Completions Per Month
NVMe
Fast Local Storage
OpenAI
Compatible API

Your source code stays on your server — build a private AI coding assistant with no third-party data processing.

Supported AI Coding Assistant Tools

Connect any of these tools to your self-hosted coding model via an OpenAI-compatible API endpoint. All work with Ollama and vLLM out of the box.

Continue
VS Code & JetBrains
Open-source AI coding assistant extension. Tab completions, inline chat, and multi-file edits with any OpenAI-compatible backend.
Cline / Roo Code
VS Code Extension
Autonomous coding agent in VS Code. Reads your codebase, creates files, runs commands, and iterates on tasks using your self-hosted model.
Aider
Terminal
AI pair programmer in your terminal. Edits multiple files, works with git, and supports any OpenAI-compatible API — perfect for self-hosted setups.
Open Interpreter
Terminal
Natural language coding agent that writes and executes code locally. Connect it to your private model for a fully offline AI coding workflow.
TabbyML
Self-Hosted Platform
Self-hosted AI coding assistant with a built-in completion engine, chat, and retrieval-augmented generation for repo-aware responses.
SWE-agent
Autonomous Agent
Princeton’s autonomous software engineering agent. Resolves GitHub issues, writes patches, and runs tests — all powered by your self-hosted model backend.
OpenHands
Autonomous Agent
Open-source AI software development agent. Browses, codes, and executes in a sandbox. Self-host the model backend for full privacy and no API fees.
Custom API Integration
Any Client
Any tool that supports the OpenAI API format can connect to your self-hosted model — VS Code extensions, JetBrains plugins, CI/CD scripts, or custom apps.

All tools connect via the standard OpenAI-compatible /v1/chat/completions endpoint exposed by Ollama and vLLM.

Best GPUs for AI Coding Assistants

Recommended configurations for self-hosted coding assistants at different team sizes and budgets.

RTX 4060 Ti · 16GB
16 GB VRAM
Solo Developer Copilot
Run Qwen2.5-Coder 7B with fast completions for a personal IDE assistant. 16GB also fits StarCoder2 15B for higher-quality suggestions at a budget-friendly price point.
Qwen2.5-Coder 7BStarCoder2 15BDeepSeek Coder 6.7B
Configure →
RTX 3090 · 24GB
24 GB VRAM
Team Coding Assistant
Best value for small-to-medium teams. 24GB runs Qwen2.5-Coder 32B at Q4 or Codestral 22B — excellent quality code completions serving multiple developers concurrently.
Qwen2.5-Coder 32B Q4Codestral 22BCode Llama 34B Q4
Configure →
RTX 5090 · 32GB
32 GB VRAM
Low-Latency Production
Blackwell 2.0 delivers the fastest completion speeds. 32GB GDDR7 runs Qwen2.5-Coder 32B comfortably — ideal for teams that need sub-200ms response times in the IDE.
Qwen2.5-Coder 32BDeepSeek Coder V2Codestral 22B
Configure →
RTX 6000 PRO · 96GB
96 GB VRAM
Enterprise / Large Models
96GB fits Code Llama 70B at full Q4, DeepSeek-V3 at aggressive quantisation, or multiple smaller models simultaneously. Built for enterprise coding assistant deployments.
Code Llama 70BDeepSeek-V3DeepSeek-R1
Configure →

AI Coding Assistant Hosting Pricing

Flat monthly pricing for a dedicated GPU server. No per-seat fees, no per-token charges, no usage caps.

RTX 3050 · 6GBStarter
ArchitectureAmpere
VRAM6 GB GDDR6
FP326.77 TFLOPS
BusPCIe 4.0 x8
~18
tok/s · StarCoder2 3B Q4Good for lightweight 1.5B–3B models
From £69.00/mo
Configure
RTX 4060 · 8GBPopular Pick
ArchitectureAda Lovelace
VRAM8 GB GDDR6
FP3215.11 TFLOPS
BusPCIe 4.0 x8
~50
tok/s · Qwen2.5-Coder 7B Q4Runs 7B models well for solo dev
From £79.00/mo
Configure
RTX 5060 · 8GBBudget
ArchitectureBlackwell 2.0
VRAM8 GB GDDR7
FP3219.18 TFLOPS
BusPCIe 5.0 x8
~68
tok/s · Qwen2.5-Coder 7B Q4GDDR7 bandwidth boost
From £89.00/mo
Configure
RTX 4060 Ti · 16GBBest Value
ArchitectureAda Lovelace
VRAM16 GB GDDR6
FP3222.06 TFLOPS
BusPCIe 4.0 x8
~65
tok/s · Qwen2.5-Coder 7B Q416GB fits StarCoder2 15B
From £99.00/mo
Configure
RX 9070 XT · 16GBAMD RDNA 4
ArchitectureRDNA 4.0
VRAM16 GB GDDR6
FP3248.66 TFLOPS
BusPCIe 5.0 x16
~92
tok/s · Qwen2.5-Coder 7B Q4ROCm / Ollama ready
From £129.00/mo
Configure
RTX 3090 · 24GBMost Popular
ArchitectureAmpere
VRAM24 GB GDDR6X
FP3235.58 TFLOPS
BusPCIe 4.0 x16
~80
tok/s · Qwen2.5-Coder 7B Q4Fits Codestral 22B, 32B Q4
From £139.00/mo
Configure
Arc Pro B70 · 32GBNew
ArchitectureXe2
VRAM32 GB GDDR6
FP3222.9 TFLOPS
BusPCIe 5.0 x16
~72
tok/s · Qwen2.5-Coder 7B Q432GB fits 32B code models
From £179.00/mo
Configure
RTX 5080 · 16GBHigh Throughput
ArchitectureBlackwell 2.0
VRAM16 GB GDDR7
FP3256.28 TFLOPS
BusPCIe 5.0 x16
~135
tok/s · Qwen2.5-Coder 7B Q4Blackwell performance
From £199.00/mo
Configure
Radeon AI Pro R9700 · 32GBAMD Pro
ArchitectureRDNA 4
VRAM32 GB GDDR6
FP3249.0 TFLOPS
BusPCIe 5.0 x16
~90
tok/s · Qwen2.5-Coder 7B Q432GB for larger models
From £249.00/mo
Configure
RTX 5090 · 32GBFastest
ArchitectureBlackwell 2.0
VRAM32 GB GDDR7
FP32104.8 TFLOPS
BusPCIe 5.0 x16
~155
tok/s · Qwen2.5-Coder 7B Q4Sub-200ms IDE completions
From £399.00/mo
Configure
Ryzen AI MAX+ 395 · 96GB96GB Unified
ArchitectureRDNA 3.5 APU
VRAM96 GB Unified
FP3225.8 TFLOPS
BusUnified Memory
~62
tok/s · Qwen2.5-Coder 7B Q496GB for very large models
From £499.00/mo
Configure
RTX 6000 PRO · 96GBEnterprise
ArchitectureBlackwell 2.0
VRAM96 GB GDDR7
FP32126.0 TFLOPS
BusPCIe 5.0 x16
~150
tok/s · Code Llama 70B Q4Fits 70B+ at full Q4
From £899.00/mo
Configure

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, cooling, and configuration. See benchmark methodology →

How Much Can You Save vs Copilot & Coding APIs?

For teams with sustained coding assistant usage, a flat-rate dedicated GPU server is often significantly cheaper than per-seat or per-token alternatives.

Hosted Copilot / API Pricing

Costs scale with every developer and every request
GitHub Copilot Business~$19/user/mo
Cursor Pro~$20/user/mo
Codeium Teams~$15/user/mo
15 devs × Copilot Business~$285/mo

Self-Hosted Coding Assistant

Fixed monthly rate — unlimited developers, unlimited completions
RTX 3090 · Qwen2.5-Coder 32B Q4Fixed/mo
RTX 4060 Ti · Qwen2.5-Coder 7BFixed/mo
RTX 5090 · Codestral 22BFixed/mo
15 devs × heavy usageSame flat rate

Example: 15-Developer Team

Per-seat route: 15 developers × $19/user/month for GitHub Copilot Business = $285/month (~£225/month). Scale to 30 developers and the cost doubles — plus you still have no control over which model is used or where your code is processed.
Self-hosted route: A dedicated RTX 3090 running Qwen2.5-Coder 32B at Q4 serves the same team with unlimited completions, inline chat, and code generation at a fixed monthly cost — and you choose which model powers the experience.
Privacy bonus: Your source code, internal APIs, and proprietary logic never leave your server. No third-party data processing agreements required — critical for regulated industries and security-conscious organisations.

Cost estimates are indicative based on publicly listed pricing at time of writing. Actual savings depend on team size, usage patterns, and the specific service used. GPU server prices retrieved live from the GigaGPU portal.

Why Self-Host a Coding Assistant Instead of Using Copilot?

A self-hosted AI coding assistant on dedicated GPU hardware vs hosted per-seat services — here’s how they compare.

Hosted Copilot / Per-Seat

Source code privacySent to third party
Pricing modelPer seat / per user
Cost at scaleGrows per developer
Model choiceProvider decides
CustomisationLimited or none
Data residencyUsually US-hosted

Self-Hosted on Dedicated GPU

Source code privacyNever leaves your server
Pricing modelFixed monthly cost
Cost at scaleSame flat rate
Model choiceYou pick the model
CustomisationFull fine-tuning access
Data residencyUK data centre

Source Code Privacy Matters

Hosted copilot route: Every code completion sends your source code, file context, and repository structure to a third-party server. For proprietary codebases, regulated industries, or security-sensitive projects, this creates compliance and intellectual property risk.
Self-hosted route: Your code stays on your own private GPU server. No data leaves your infrastructure — ideal for financial services, defence, healthcare, legal, and any team that treats source code as confidential intellectual property.

Self-hosting is particularly advantageous for coding assistants because the data involved — source code, repository context, internal APIs, business logic — is often the most sensitive intellectual property a company owns.

AI Coding Assistant Hosting Use Cases

From private IDE copilots to team-wide code review — dedicated GPU servers power every AI coding assistant workflow.

Private IDE Copilot

Replace GitHub Copilot with a self-hosted alternative. Deploy Qwen2.5-Coder or Codestral behind an OpenAI-compatible API and connect Continue, Cline, or TabbyML to your own server — unlimited completions, zero per-seat fees.

Team-Wide Coding Assistant

Give your entire engineering team access to a shared AI coding assistant. A single GPU server can serve multiple developers concurrently with real-time completions, inline chat, and multi-file edits — all at a fixed monthly cost.

Agentic Coding Workflows

Power Aider, Roo Code, SWE-agent, or OpenHands with your own model backend. Agentic tools make many sequential model calls — fixed GPU pricing makes these workflows economically viable where API fees would be prohibitive.

Automated Code Review

Integrate your self-hosted coding model into CI/CD pipelines to review pull requests, detect bugs, and suggest improvements automatically. Process every PR at a fixed cost — no matter how active your team is.

Test Generation Pipelines

Point a coding assistant at your source files and generate unit tests, integration tests, and edge case coverage automatically. Self-hosting means you can process entire repositories without per-token cost concerns.

Secure Coding for Regulated Industries

Financial services, healthcare, defence, and legal teams can run private AI coding assistants without sending source code to external providers. UK-based servers support data residency requirements.

Embedded Coding AI in SaaS

Integrate code completion and generation into your own product — online IDEs, developer platforms, learning tools, or no-code builders. Self-hosted models via API hosting let you offer AI coding features without per-user API costs eating your margins.

Repo-Aware Internal Copilots

Build a coding assistant that understands your internal APIs, conventions, and codebase structure. Combine a self-hosted model with RAG and LangChain or LlamaIndex for context-aware, repo-specific responses.

Compatible Frameworks, Tools & IDE Integrations

Full root access — install any framework, runtime, or IDE integration in minutes.

vLLM Ollama Continue (VS Code / JetBrains) Cline / Roo Code Aider TabbyML Open Interpreter SWE-agent OpenHands PyTorch LangChain LlamaIndex Hugging Face Transformers llama.cpp OpenAI-Compatible API Neovim / Avante

Deploy Your AI Coding Assistant in 4 Steps

From order to running code completions in your IDE in under 30 minutes.

01

Choose Your GPU

Pick the GPU that fits your team size, preferred model, and budget. Select your OS (Ubuntu 22/24, Debian, Windows) and NVMe storage.

02

Install Runtime & Model

Install Ollama (curl -fsSL https://ollama.com/install.sh | sh) or vLLM. Pull your chosen coding model — Qwen2.5-Coder, Codestral, DeepSeek Coder, or any open-weight option.

03

Connect Your IDE

Install Continue, Cline, or your preferred extension. Point it at your server’s OpenAI-compatible API endpoint. Configure TLS with Nginx or Caddy if needed.

04

Code with AI

Start coding — tab completions, inline chat, multi-file edits, and agentic workflows. Unlimited usage, zero per-call fees. Add more developers at no extra cost.

AI Coding Assistant Hosting — Frequently Asked Questions

Everything you need to know about self-hosting your own AI coding assistant on dedicated GPU hardware.

AI coding assistant hosting means running your own private coding assistant — similar to GitHub Copilot — on a dedicated GPU server. You deploy an open-source coding model, expose an OpenAI-compatible API, and connect it to your IDE via tools like Continue, Cline, or Aider. Instead of paying per-seat fees, you get unlimited completions at a flat monthly cost.
Yes. Open-source coding models like Qwen2.5-Coder, Codestral, and DeepSeek Coder can be deployed on a GPU server via Ollama or vLLM. Connect an IDE extension like Continue or Cline to your server’s API endpoint and you have a fully private, self-hosted coding assistant with tab completions, inline chat, and multi-file editing.
For IDE code completion, Qwen2.5-Coder (7B or 32B) and Codestral 22B offer the best balance of quality and speed. Smaller models like DeepSeek Coder 6.7B and StarCoder2 3B are good for lightweight, fast completions on budget GPUs. For agentic coding workflows, larger models like DeepSeek-R1 or Code Llama 70B provide stronger reasoning capabilities. See our code model hosting page for detailed model recommendations.
Both Continue and Cline support custom API endpoints. After deploying your coding model with Ollama or vLLM (which expose an OpenAI-compatible API by default), you configure the extension to point at http://your-server-ip:11434 (Ollama) or http://your-server-ip:8000 (vLLM). Continue supports both VS Code and JetBrains IDEs. Cline works in VS Code.
For teams with more than a few developers, typically yes. GitHub Copilot Business costs ~$19/user/month — a 15-developer team pays ~$285/month. A dedicated RTX 3090 running Qwen2.5-Coder 32B serves the same team with unlimited usage at a fixed monthly cost, and the per-developer cost drops as you add more users.
Yes — that’s one of the primary advantages. With a self-hosted coding assistant, your source code never leaves your server. No data is sent to GitHub, Microsoft, OpenAI, or any third party. This is critical for teams handling proprietary code, regulated data, or sensitive intellectual property.
For a solo developer, an RTX 4060 Ti (16GB) running Qwen2.5-Coder 7B provides fast completions. For small-to-medium teams, the RTX 3090 (24GB) is the best value — it runs Qwen2.5-Coder 32B at Q4 with good concurrency. For larger teams or production workloads, the RTX 5090 (32GB) delivers the fastest response times.
Yes. Aider supports any OpenAI-compatible API endpoint. After deploying your coding model with Ollama or vLLM, configure Aider to use your server as the API base URL. This gives you Aider’s full multi-file editing and git integration capabilities powered entirely by your own private infrastructure.
With sufficient VRAM, yes. Ollama can swap between models on demand, loading whichever model is requested. For dedicated multi-model serving, higher-VRAM GPUs like the RTX 5090 (32GB) or RTX 6000 PRO (96GB) let you keep multiple models loaded simultaneously for different use cases.
Yes. Start with a single GPU server and add more as your team grows. You can run multiple inference servers behind a load balancer, or deploy different models on different servers for specialised workloads. The flat-rate pricing means adding more developers to an existing server costs nothing extra.

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 AI coding assistants, private IDE copilots, agentic coding workflows, automated code review, and any developer tooling powered by AI — with no shared resources and no per-seat fees.

Get in Touch

Have questions about which GPU is right for your team’s coding assistant? Our team can help you choose the right configuration for your model, team size, and budget.

Contact Sales →

Or browse the knowledgebase for setup guides on Ollama, vLLM, Continue, and more.

Start Hosting Your AI Coding Assistant Today

Flat monthly pricing. Full GPU resources. UK data centre. Deploy your own private Copilot alternative in under an hour.

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