Source: Ollama
Granite Code brings enterprise-grade code intelligence to your private infrastructure. 116 programming languages, fill-in-the-middle completion, and model sizes from 3B to 34B — all running locally with Ollama so your proprietary source code never touches an external server.
GitHub Copilot, Amazon CodeWhisperer, and similar cloud-based code AI tools have a fundamental problem for organizations building proprietary software: every code completion request sends your source code to an external server. Your algorithms, your architecture decisions, your unreleased features — all transmitted to third-party infrastructure to generate a suggestion.
For startups with early-stage intellectual property, defense contractors with controlled technical data, financial firms with proprietary trading algorithms, and enterprises with competitive technology advantages, this is not an acceptable trade-off. The productivity gains from AI-assisted coding are real, but not if they come at the cost of your most valuable assets.
Granite Code running on Ollama through NoCloudGPT infrastructure eliminates this problem entirely. The model runs on hardware you control. Code completions are generated locally. Nothing leaves your network perimeter. Your source code stays proprietary.
IBM reinforces this with what it calls "code-safe" training for Granite Code — the model is trained exclusively on permissively licensed code with documented provenance, addressing both the data transmission concern and the training data IP concern that has made legal departments cautious about AI-assisted development.
IBM releases Granite Code in four sizes, each targeting a different hardware tier and use case. All variants support the same 116-language corpus and fill-in-the-middle completion capability — the size difference primarily affects reasoning depth on complex multi-file problems.
Designed for developer workstations and CI/CD pipeline integration. Fast inference for inline completion, docstring generation, and single-function tasks.
The balanced choice for shared team servers. Handles multi-function code generation, refactoring tasks, and code review assistance with strong accuracy.
Mid-tier flagship balancing power and cost. Designed for complex algorithm implementation, large codebase navigation, and cross-language translation tasks.
IBM's most capable code model. Competes with the strongest commercial code AI systems on HumanEval and MultiPL-E benchmarks while running entirely on your infrastructure.
Each Granite Code size is released in two variants. Base models are optimized for fill-in-the-middle (FIM) completion in IDE integrations — they complete code in context without conversational overhead. Instruct models are fine-tuned for natural language instruction following, making them appropriate for chat-based code assistants, code explanation, and review workflows. For IDE plugins like Continue.dev or Aider, the base model delivers lower latency; for chat interfaces, use the instruct variant.
Most AI code tools optimize for Python, JavaScript, and TypeScript because that is where benchmark datasets are concentrated. Enterprise codebases rarely look like benchmarks. They contain legacy COBOL alongside modern Rust, SQL embedded in Java, YAML configuration files, shell scripts, and domain-specific languages that general-purpose code AI struggles with.
IBM trained Granite Code on 116 programming languages specifically to address enterprise codebase diversity. The training corpus prioritizes languages commonly found in financial systems, infrastructure automation, and industrial software — not just the languages that perform well on academic benchmarks.
IBM's deep history in enterprise computing means Granite Code has meaningful COBOL capability — a differentiator no other open-weight code model can credibly claim. For financial institutions and government agencies maintaining legacy COBOL systems, Granite Code can assist with modernization analysis, documentation generation, and incremental refactoring in ways that Python-centric models cannot.
Standard language model completion works left-to-right: given what comes before the cursor, predict what comes next. This works acceptably for autocomplete but fails for the most common editing pattern in real development — inserting code in the middle of existing functions.
Fill-in-the-middle (FIM) trains the model to use both prefix and suffix context. Granite Code sees what comes before the cursor and what comes after it, then generates the insertion that makes the complete code block logically consistent. This dramatically improves completion accuracy for:
Write the function signature and the return statement — Granite Code fills in the implementation logic with awareness of both.
Insert try/catch blocks, input validation, or null checks into existing functions without breaking the surrounding logic.
Generate unit tests that fit between existing test fixtures with awareness of the test runner patterns already in use.
Add docstrings, inline comments, and type annotations that match existing documentation style in the file.
Granite Code uses a standard FIM token format compatible with Ollama's API:
IDE integrations like Continue.dev handle this formatting automatically — developers interact through standard completion UI without configuring FIM syntax manually.
Granite Code integrates with the same IDE plugins used for cloud-based code AI, but routes all requests to your local Ollama server instead of external APIs. Setup takes minutes and requires no code changes to existing development workflows.
The most feature-complete open-source AI coding assistant. Supports chat, inline completion, and codebase indexing. Configure it to point at your Ollama endpoint with Granite Code as the provider.
Command-line AI pair programmer that works with your git repository. Excellent for multi-file refactoring tasks and feature implementation from natural language specs.
Open-source GitHub Copilot replacement that runs as a server for your entire engineering team. Can be configured to use Granite Code through Ollama as the backend model, providing team-wide AI completion from a single private deployment.
Ollama exposes an OpenAI-compatible API endpoint, meaning any tool that supports custom OpenAI API bases works with Granite Code without modification. This covers most commercial IDE plugins that allow custom endpoints.
Organizations working on ITAR-controlled technology, classified systems, or sensitive government contracts cannot use cloud-based code AI without creating export control and data classification violations. Granite Code on air-gapped or network-isolated infrastructure provides AI-assisted development within the required security boundary. IBM's government sector credentials make Granite a defensible vendor choice for contractor procurement processes.
Trading algorithms, risk models, and pricing engines represent competitive advantages worth hundreds of millions of dollars. Firms that have built proprietary quantitative systems cannot allow that code to be transmitted to external AI services — even with contractual data protection guarantees. Private Granite Code deployment allows quant developers and financial engineers to use AI assistance on their most sensitive code without IP exposure.
Healthcare software often handles PHI directly in the codebase — database schemas, query strings, configuration files, and test fixtures may contain protected health information or reveal system architecture that creates HIPAA exposure. Development teams working on EHR systems, medical devices, and clinical platforms need code AI that operates entirely within their security perimeter.
Any company whose competitive advantage lives in software — whether that is a proprietary recommendation engine, a novel data processing pipeline, or a unique user experience implementation — has legitimate reasons to keep source code off external servers. Private Granite Code provides the productivity of AI-assisted development without the IP exposure of cloud-routed code completion.
Granite Code competes directly with open-weight code models from Meta (Code Llama), Alibaba (Qwen Coder), and DeepSeek on standard code generation benchmarks. The 34B flagship model achieves performance competitive with models significantly larger in parameter count, reflecting IBM's optimization focus on enterprise coding tasks.
| Model | HumanEval Pass@1 | MultiPL-E | Languages | FIM Support |
|---|---|---|---|---|
| Granite Code 34B | ~79% | Strong | 116 | Yes |
| Granite Code 20B | ~72% | Good | 116 | Yes |
| Granite Code 8B | ~67% | Good | 116 | Yes |
| Granite Code 3B | ~55% | Moderate | 116 | Yes |
Note: HumanEval Pass@1 figures are approximate and drawn from IBM's published model cards and third-party evaluations. Performance varies by task type and prompt format. Enterprise users should evaluate on their specific codebase and language mix.
Pull Granite Code 8B (balanced choice for most teams)
ollama pull granite-code:8b
Pull the instruct variant for chat-based code assistance
ollama pull granite-code:8b-instruct
Pull the flagship 34B model (requires 24GB+ VRAM)
ollama pull granite-code:34b
Test with a code generation prompt
ollama run granite-code:8b-instruct "Write a Python function that validates an email address using regex and returns True or False"
API call for programmatic integration
curl http://localhost:11434/api/generate -d '{"model": "granite-code:8b-instruct", "prompt": "Explain this function and suggest improvements: [CODE]"}'
Granite Code 3B or 8B on a consumer GPU
Granite Code 20B serving 5–20 concurrent developers
Granite Code 34B serving engineering org
Automated code review and test generation
General-purpose IBM Granite for document analysis, compliance, and enterprise workflows.
Explore →Alibaba's code-specialized family. Strong Python/JS performance, different training data provenance.
Compare →Top HumanEval scores in the open-weight space. Compare with Granite Code 34B for your stack.
Compare →Deploy Granite Code on your infrastructure. AI-assisted development with zero source code exposure — your algorithms stay yours.