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LLAMA 3.3 70B

Tabnine

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Secure code generation

Llama enables deployments on private clouds and air-gapped hardware.

Transparent behavior

Llama’s open weights give customers access and insight into the model.

Brand-building innovation

Tabnine is the go-to for secure, cutting-edge AI code generation.
*All results are self-reported and not identifiably repeatable. Generally, expected individual results will differ.

CASE STUDY

Bringing secure code generation to air-gapped deployments with Llama

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At a glance

Industry

Technology

Use case

Automating coding tasks in secure industries

Goal

Deploy code assistant on private clouds and air-gapped hardware

Llama version

Llama 3.3 70B

Deployment

Private cloud and on-premises infrastructure

THEIR STORY

Earning developer trust, one line at a time

Tabnine is a human-in-the-loop, contextually aware AI software development platform that generates and validates software like top engineers. The platform accelerates engineering velocity and software quality with customizable AI tools that support an organization’s coding patterns, standards and expectations.

THEIR GOAL

Bring code generation to high-security industries

Tabnine is a highly-ranked AI-powered software development platform that generates code, designs test plans and validates and documents its work. It’s model-agnostic, which allows developers to work with their preferred AI tooling via an API call. It’s an elegant solution for most clients, but for clients who need airtight security, Tabnine wasn’t an option.

THEIR SOLUTION

Deliver maximum security and transparency with open-source Llama

With Llama, Tabnine was able to deploy its entire platform, including Llama 3.3 70B, to private cloud instances and air-gapped infrastructure for maximum security. In addition to strengthening security, Llama’s open weights enable Tabnine and its customers to audit model behavior, tune for specific codebases and customize the model to improve accuracy and performance.
their solution graphic

THEIR APPROACH

Multi-agent expertise and RAG produce context-aware results

Tabnine brings AI assistance to most integrated development environments (IDEs). Under the hood, a retrieval-augmented generation (RAG) workflow provides relevant context for user prompts and injects them into a large language model (LLM) for response generation. A control layer applies quality, security and governance rules to the model’s output.Tabnine is container-based and cloud-native, so customers can deploy on the infrastructure of their choice and scale on demand. For high-security use cases, Tabnine recommends customers pair the platform with Llama 3.3 70B and deploy it on their private cloud. For industries that require maximum security — like healthcare, finance and defense — Tabnine supports on-premises deployments on Dell PowerEdge servers equipped with NVIDIA L40S or H100 GPUs.
their approach graphic
For maximum security, clients deploy Tabnine and Llama on private cloud or air-gapped infrastructure. Privatemode AI verifies the integrity of each step of the confidential computing service.

THEIR SUCCESS

Securing code bases and new customers with Llama

Integrating Llama provided Tabnine customers with high-security options for AI-assisted code generation and much more. Open source Llama helped the Tabnine team accelerate feature delivery and respond quickly to customer needs, positioning the platform as a developer-first player in the market that is serious about security and innovation. This powerful reputation is winning new business across sectors.
  • Secure code generation — Llama enables deployments on private clouds and air-gapped hardware.
  • Transparent behavior — Llama’s open weights give customers access and insight into the model.
  • Brand-building innovation — Tabnine is the go-to for secure, cutting-edge AI code generation.
*All results are self-reported and not identifiably repeatable. Generally, expected individual results will differ.
“Being able to offer Llama in self-hosted, VPC and air-gapped environments helps us earn trust faster with highly regulated customers. Even before they run a pilot, the fact that we’re leveraging models that can be audited, governed and controlled has improved our positioning in procurement and architecture discussions.”

Chris Good — Director of Product Marketing, Tabnine

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