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Code Model Hosting

Host Open Source Coding Models on Dedicated UK GPU Servers

Run DeepSeek Coder, Qwen2.5-Coder, Code Llama, StarCoder2, and Codestral on your own bare metal GPU server. Build private code completion APIs, IDE copilots, and agentic coding workflows — fixed monthly pricing, no per-token fees.

What is Code Model Hosting?

Code model hosting means running open-weight code generation and code completion models — such as DeepSeek Coder, Qwen2.5-Coder, Code Llama, or StarCoder2 — 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 vLLM, Ollama, or Hugging Face Transformers and expose an OpenAI-compatible API for your IDE, coding agent, or internal developer tools — no shared resources, no usage caps, no source code leaving your infrastructure.

Self-hosted coding models are ideal for teams building private AI coding assistants, running code review and test generation pipelines, powering agentic workflows with tools like Aider or Continue, or embedding code generation into SaaS products — especially when sustained usage makes per-token or per-seat pricing expensive.

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

Built for private code model hosting — dedicated GPU hardware, not shared inference queues.

Supported Code Models

Deploy the most capable open-weight coding models. Compatibility depends on GPU VRAM, quantisation, and framework support.

DeepSeek Coder V2
DeepSeek AI
236B MoECode CompletionInstruct
Qwen2.5-Coder 32B
Alibaba
32BIDE AssistantMultilingual
Code Llama 70B
Meta
70BCode CompletionInstruct
StarCoder2 15B
BigCode
15BMultilingual CodingFast
Codestral 22B
Mistral AI
22BCode GenerationFast Inference
DeepSeek-V3
DeepSeek AI
685B MoECodingReasoning
Qwen2.5-Coder 7B
Alibaba
7BCode CompletionFast
Code Llama 13B
Meta
13BInstructRepo Assistant
DeepSeek-R1
DeepSeek AI
671B / 70BReasoningAgentic Coding
Phi-4 14B
Microsoft
14BReasoningCode
StarCoder2 3B
BigCode
3BFast InferenceLightweight
Code Llama 34B
Meta
34BLarge ContextRefactoring
DeepSeek Coder 6.7B
DeepSeek AI
6.7BCode CompletionLightweight
Codestral Mamba
Mistral AI
7BFast InferenceLong Context
Qwen2.5-Coder 1.5B
Alibaba
1.5BEdge / LightweightFast

Most open-weight coding models supported by Ollama, vLLM, Hugging Face Transformers, or llama.cpp are deployable. Compatibility depends on VRAM, quantisation, and framework support.

Best GPUs for Code Model Hosting

Recommended configurations for private coding assistants, code completion APIs, and agentic workflows.

RTX 4060 Ti
16 GB VRAM
Dev & Lightweight Assistants

16GB comfortably fits Qwen2.5-Coder 7B, StarCoder2 15B at Q4, or Code Llama 13B. Ideal for individual developers or small teams running a private coding assistant during development.

Qwen2.5-Coder 7B StarCoder2 15B Q4 Code Llama 13B
Configure RTX 4060 Ti →
RTX 3090
24 GB VRAM
Best Value for Production

24GB runs Qwen2.5-Coder 32B at Q4, Codestral 22B, or Code Llama 34B at Q4. The sweet spot for most production code assistant hosting workloads with excellent throughput-to-cost.

Qwen2.5-Coder 32B Q4 Codestral 22B Code Llama 34B Q4
Configure RTX 3090 →
RTX 5090
32 GB VRAM
High-Throughput Production

Blackwell 2.0 delivers the fastest single-GPU inference for production code completion APIs. 32GB GDDR7 handles Qwen2.5-Coder 32B at Q4 with headroom, or Code Llama 70B at Q2 — ideal for low-latency IDE integrations serving multiple developers.

Qwen2.5-Coder 32B Code Llama 70B Q2 DeepSeek Coder V2
Configure RTX 5090 →
Radeon AI Pro R9700
32 GB VRAM
32GB AMD Alternative

RDNA 4 architecture with 32GB and 644 GB/s bandwidth — a competitive alternative for teams comfortable with ROCm or needing 32GB VRAM at a lower price point than the RTX 5090.

Qwen2.5-Coder 32B Q4 Code Llama 70B Q2 ROCm ready
Configure R9700 →

Which GPU Do I Need for Code Models?

Answer three quick questions and we’ll recommend the right server for your coding workload.

Question 1 of 3
What are you building?
Question 2 of 3
Is this for development or production?
Question 3 of 3
What matters most?
Recommended for your coding workload
—
—
Configure this server →

Code Model Hosting Pricing

RTX 3050 · 6GBStarter
ArchitectureAmpere
VRAM6 GB GDDR6
FP326.77 TFLOPS
BusPCIe 4.0 x8
~18
tok/s · StarCoder2 3B Q4Good for 1.5B–3B code 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 code models well
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 £189.00/mo
Configure
Radeon AI Pro R9700 · 32GBAI Pro
ArchitectureRDNA 4
VRAM32 GB GDDR6
FP3247.84 TFLOPS
BusPCIe 5.0 x16
~105
tok/s · Qwen2.5-Coder 7B Q432GB runs 32B code models
From £199.00/mo
Configure
Ryzen AI MAX+ 395 · 96GBNew
ArchitectureStrix Halo
Unified RAM96 GB LPDDR5X
FP3214.8 TFLOPS
BusPCIe 4.0
~52
tok/s · Qwen2.5-Coder 7B Q496GB shared memory pool
From £209.00/mo
Configure
RTX 5090 · 32GBFor Production
ArchitectureBlackwell 2.0
VRAM32 GB GDDR7
FP32104.8 TFLOPS
BusPCIe 5.0 x16
~210
tok/s · Qwen2.5-Coder 7B Q4Fastest code model inference
From £399.00/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 significantly with concurrent requests, context length, cooling, and configuration. See benchmark methodology →

How Much Can You Save vs Coding API Providers?

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

Per-Token / Per-Seat Pricing

Costs scale with every developer and every request
GitHub Copilot Business~$19/user/mo
OpenAI GPT-4o (code tasks)~$15 / 1M tokens
Claude Sonnet (code tasks)~$3 / 1M tokens
10 devs × heavy usage£200–£2,000+/mo

Dedicated GPU Server

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

Example: 10-Developer Team

Per-seat route: 10 developers × $19/user/month for a hosted copilot = $190/month — and that's a basic tier. Heavier API usage for code review, test generation, or agentic pipelines adds per-token costs on top.
Self-hosted route: A dedicated RTX 3090 running Qwen2.5-Coder 32B at Q4 serves the same team with unlimited completions at a fixed monthly cost — no per-seat or per-token charges regardless of how much they use it.
Privacy bonus: Your source code never leaves your server. No third-party data processing agreements needed for your proprietary codebase.

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

Code Model Hosting Cost Calculator

Estimate your monthly cost when running a self-hosted coding assistant vs paying per-token API fees.

5 developers
50 prompts/day
—
API Cost/Month
—
GPU Server/Month
—
Est. Saving/Month

Why Host Code Models Instead of Using APIs?

Self-hosted coding models on dedicated GPU hardware vs per-token API services — here's how they compare for code generation workloads.

Hosted API / Per-Seat Model

Source code privacySent to third party
PricingPer token or per seat
Cost at scaleGrows with usage
LatencyShared queue
Model controlProvider decides
Custom fine-tuningLimited or unavailable

Self-Hosted on Dedicated GPU

Source code privacyNever leaves your server
PricingFixed monthly cost
Cost at scaleSame flat rate
LatencyDedicated hardware
Model controlYou choose the model
Custom fine-tuningFull access

Source Code Privacy Matters

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

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

Code Model Hosting — GPU Performance Overview

Commercially useful benchmark framing for code inference: tokens/sec on common coding models, first-token responsiveness and suitability for IDE completion or code API traffic.

GPU VRAM DeepSeek Coder 6.7B
tokens/sec		 Qwen2.5-Coder 7B
tokens/sec		 First Token
(short code prompt)		 Best Fit Relative Capability
RTX 3050 6 GB 15–22 14–20 0.8–1.5s Lightweight 1.5B–3B code models, personal experimentation
12%
RTX 4060 8 GB 45–65 42–60 0.4–0.8s Single-dev code assistant, lightweight 7B models
38%
RTX 5060 8 GB 55–78 52–74 0.35–0.7s Budget Blackwell option for fast 7B code inference
46%
RTX 4060 Ti 16 GB 70–95 65–90 0.35–0.7s Private dev copilots, low-traffic IDE completion
58%
RX 9070 XT 16 GB 80–108 76–104 0.3–0.6s AMD 16GB option for code completions via ROCm
65%
RTX 3090 24 GB 95–125 90–120 0.25–0.55s Best-value production code APIs and team copilots
74%
Arc Pro B70 32 GB 68–90 65–86 0.35–0.7s 32GB Intel option for larger code models
55%
RTX 5080 16 GB 110–148 105–140 0.2–0.5s High-throughput Blackwell for fast 7B code APIs
88%
Radeon AI Pro R9700 32 GB 90–120 88–116 0.28–0.6s High-VRAM repo-aware stacks and larger contexts
78%
Ryzen AI MAX+ 395 96 GB 48–65 45–62 0.4–0.8s 96GB unified memory for very large code models
40%
RTX 5090 32 GB 125–165 120–155 0.18–0.45s Low-latency production inference and more concurrency
100%
RTX 6000 PRO 96 GB 110–145 (70B) 105–140 (70B) 0.3–0.7s (70B) Code Llama 70B Q4, enterprise large-model deployments
90%
Methodology note: these are practical reference ranges for self-hosted coding inference rather than marketing peak numbers. Figures assume a single active model instance, typical 4-bit or similar deployment settings where appropriate, short-to-medium code prompts, and API-style generation rather than synthetic maximum throughput. Actual results vary with prompt length, context window, quantisation, runtime choice, batch size, tokenizer overhead and the framework you use for serving. For example, an IDE completion endpoint via vLLM or Ollama behaves differently from a heavier repo-aware agent using retrieval, tools and longer file context. The important commercial point is relative fit: lighter GPUs suit dev and internal copilots, while RTX 3090 and RTX 5090 class servers are better for sustained production coding APIs.

Code Model Hosting Use Cases

From private IDE copilots to automated code review pipelines — dedicated GPU servers power every coding AI workload.

Private AI Coding Assistants

Run a self-hosted alternative to GitHub Copilot for your team. Deploy Qwen2.5-Coder or Codestral behind an OpenAI-compatible API and connect it to Continue, Cline, or any IDE plugin — unlimited completions, zero per-seat fees. See our AI coding assistant hosting guide.

IDE Code Completion APIs

Expose a fast code completion endpoint for VS Code, JetBrains, or Neovim. Self-hosted code models deliver consistent sub-second latency without shared-queue variability — critical for keeping developers in flow.

Internal Developer Copilots

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

Automated Test Generation

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

Code Review & Refactoring

Automate pull request reviews, detect code smells, and suggest refactoring improvements. Run code models against diffs in CI/CD pipelines at a fixed cost — no matter how many PRs your team opens.

Agentic Coding Workflows

Power SWE-agent, OpenHands, or custom agentic coding tools with a self-hosted code model backend. Agentic workflows involve many sequential model calls — fixed pricing makes them economically viable at scale.

Ticket-to-Code & Spec-to-Code

Build pipelines that take JIRA tickets, GitHub issues, or product specs and generate initial code implementations. Ideal for internal tooling teams looking to accelerate development velocity.

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

Aider / Roo Code / Open Interpreter

Tools like Aider, Roo Code, and Open Interpreter work best with a private, fast model backend. Self-hosting eliminates rate limits and gives you full control over which model powers your terminal-based coding assistant.

Compatible Frameworks & Tools

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

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

Deploy a Code Model in 5 Steps

From order to running code completions in under 30 minutes.

01

Choose Your GPU

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

02

Server Provisioned

Your dedicated GPU server is provisioned and you receive SSH or RDP credentials. Typical deployment time is under one hour.

03

Install Runtime

Install Ollama (curl -fsSL https://ollama.com/install.sh | sh), vLLM, or your preferred inference framework. Pull your chosen code model from Hugging Face or Ollama's library.

04

Expose API Endpoint

Configure an OpenAI-compatible API endpoint via Ollama or vLLM. Set up Nginx or Caddy for TLS if needed. Point your IDE plugin, Aider, or internal tooling at your server.

05

Code & Scale

Start generating code — unlimited tokens, zero per-call fees. Scale to additional GPUs later if your team grows or throughput demands increase.

Code Model Hosting — Frequently Asked Questions

Everything you need to know about self-hosting coding models on dedicated GPU hardware.

Code model hosting means running an open-weight code generation or code completion model — such as DeepSeek Coder, Qwen2.5-Coder, Code Llama, StarCoder2, or Codestral — on your own dedicated GPU server. Instead of paying per-token or per-seat fees to a third-party API, you get unlimited inference at a flat monthly cost with full control over your data.
Yes. Open-weight coding models like Qwen2.5-Coder, DeepSeek Coder, Code Llama, and StarCoder2 can be self-hosted on any GPU server with sufficient VRAM. Install Ollama or vLLM, pull the model, and you have a running code generation endpoint in minutes.
DeepSeek Coder comes in several sizes. The 6.7B variant runs well on 8–16GB GPUs like the RTX 4060 Ti. The 33B variant at Q4 fits on 24GB (RTX 3090). DeepSeek Coder V2 is a 236B MoE model that requires 32GB+ at aggressive quantisation. Check the model card on Hugging Face for specific VRAM requirements.
Qwen2.5-Coder 7B runs comfortably on 8–16GB GPUs. Qwen2.5-Coder 32B at Q4_K_M fits well on 24GB (RTX 3090) or 32GB (RTX 5090, R9700). For production workloads with multiple concurrent users, we recommend 24GB+ for 7B models and 32GB+ for 32B models to maintain fast response times.
Absolutely. Deploy a code model on your GigaGPU server, expose an OpenAI-compatible API, and connect it to IDE plugins like Continue, Cline, or TabbyML. Your entire team can use it for code completion, chat-based assistance, and code review — with no per-seat licensing and no source code leaving your infrastructure. See our AI coding assistant hosting page for more.
For sustained usage, typically yes. A team of developers generating thousands of completions per day can quickly exceed the cost of a dedicated GPU server when paying per-token. The break-even depends on your team size, usage volume, and the specific API you'd otherwise use. Use our cost calculator above to estimate your scenario.
Yes. Extensions like Continue and Cline connect to any OpenAI-compatible API endpoint. Both Ollama and vLLM expose this format by default. Point the extension at your server's IP and port, and you'll get code completions and chat assistance directly in VS Code — all powered by your own private model.
Yes. Aider supports any OpenAI-compatible API via the --openai-api-base flag. Continue supports custom API endpoints in its configuration. Roo Code and Open Interpreter also work with OpenAI-compatible backends. Your self-hosted model plugs in seamlessly.
Yes — this is one of the main advantages. With a self-hosted code model, your source code never leaves your server. You can process private repos, internal APIs, and proprietary code without any data being sent to a third party. This is critical for regulated industries, IP-sensitive projects, and security-conscious teams.
For teams building internal dev tools — code review bots, test generators, spec-to-code pipelines — self-hosted models are typically more cost-effective and more flexible than API-based alternatives. You control the model, the context, and the deployment without dependency on external services or usage-based billing.
For most teams, the RTX 3090 (24GB) offers the best value — it runs Qwen2.5-Coder 32B at Q4 or Codestral 22B with strong throughput. For production with low latency requirements, the RTX 5090 (32GB) is the top choice. For a budget dev setup, the RTX 4060 Ti (16GB) handles 7B code models well. Use the quiz tool above for a personalised recommendation.
Yes. Agentic coding frameworks like SWE-agent, OpenHands, and custom agent loops work well with self-hosted code models. These workflows involve many sequential inference calls — fixed-cost GPU hosting makes them economically viable compared to per-token APIs where costs can spiral quickly.
Ollama is the simplest option — one-command install, built-in model management, and an OpenAI-compatible API out of the box. vLLM offers higher throughput for production workloads with features like continuous batching. Hugging Face Transformers provides the most flexibility for custom inference pipelines. All three are fully supported on GigaGPU servers.
Yes. Both Ollama and vLLM expose a REST API compatible with the OpenAI format (/v1/chat/completions). You can point any existing OpenAI SDK, IDE extension, or internal tool at your server's IP and it will work without code changes — making migration from closed-source APIs straightforward.
Yes — all of these models are supported through Ollama, vLLM, and Hugging Face Transformers. Compatibility depends on available VRAM and quantisation choice. You have full root access to install any framework and pull any model from Hugging Face or Ollama's model library.
Yes. Start with a single GPU server and add more as your team or traffic grows. You can run multiple inference servers behind a load balancer, or deploy different models on different servers (e.g. a fast 7B for completions and a larger 32B for chat-based assistance). Contact our sales team for multi-GPU configurations.

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 code models, private coding assistants, code review pipelines, agentic coding workflows, and any AI-powered developer tooling — with no shared resources and no token fees.

Get in Touch

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

Contact Sales →

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

Start Hosting Your Code Model Today

Flat monthly pricing. Full GPU resources. UK data centre. Deploy DeepSeek Coder, Qwen2.5-Coder, Code Llama, and more in under an hour.

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