# Running Large Language Models Inside SAP S/4HANA: A Pure ABAP-Implemented LLM Inference Engine

> abap-llm-engine is a groundbreaking project that enables direct execution of large language models (LLMs) inside SAP S/4HANA systems. Built entirely with pure ABAP code, this project features a complete Transformer inference engine without external dependencies like Python, llama.cpp, or ONNX, bringing native AI capabilities to traditional enterprise ERP systems.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-03T22:09:11.000Z
- 最近活动: 2026-04-03T22:18:19.303Z
- 热度: 150.8
- 关键词: SAP, ABAP, LLM, Transformer, 企业级AI, S/4HANA, 本地推理, HANA加速
- 页面链接: https://www.zingnex.cn/en/forum/thread/sap-s-4hana-abap-llm
- Canonical: https://www.zingnex.cn/forum/thread/sap-s-4hana-abap-llm
- Markdown 来源: floors_fallback

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## 【Introduction】Pure ABAP-Implemented LLM Inference Engine: Running Large Language Models Inside SAP S/4HANA

abap-llm-engine is the world's first LLM inference engine fully implemented based on ABAP, allowing direct execution of large language models (e.g., SmolLM2-135M) inside SAP S/4HANA systems. Without external dependencies like Python, llama.cpp, or ONNX, this project leverages HANA database acceleration capabilities to bring native AI to traditional enterprise ERP systems.

## Project Background: Pain Points of AI Integration in Traditional SAP Systems

Traditional SAP systems lack native AI capabilities. External LLM deployment solutions often rely on Python environments, third-party inference libraries, or external API calls, leading to issues like data security risks, high integration complexity, and incompatibility with air-gapped environments. The abap-llm-engine project aims to address these pain points by natively embedding AI capabilities inside SAP systems.

## Technical Architecture and Core Components

The project uses a modular class structure design, with core components including:
- ZCL_LLM_ENGINE: Inference process coordinator
- ZCL_LLM_BPE_TOKENIZER: BPE tokenizer
- ZCL_LLM_TENSOR: Tensor operation class
- ZCL_LLM_TRANSFORMER_BLOCK: Transformer layer implementation (including RMS normalization, ROPE positional encoding, grouped query attention, etc.)
- ZCL_LLM_HANA_ACCEL: HANA acceleration module

Model specifications: 135 million parameters, Llama architecture (30 layers, 576 hidden dimensions, 9 attention heads), ~250MB memory usage after INT8 quantization, 8192-token context window.

## Operation Modes and Performance

The project offers three operation modes:
1. **Pure ABAP Mode**: Speed of 5-30 seconds per token, no special configuration needed, suitable for development testing or scenarios without HANA acceleration.
2. **HANA AMDP Acceleration Mode**: Speed of 0.5-3 seconds per token, offloads matrix operations to HANA parallel engine for significant speed improvement.
3. **Shared Memory Mode**: 30% faster than the basic mode; weight sharing reduces memory usage and enhances concurrency.

## Deployment and Integration Advantages

This project features zero external dependencies and native SAP integration:
- No need for Python, third-party libraries, or external APIs
- Direct access to SAP data dictionary (e.g., DD03L) to ensure accurate generated content
- Deployable via SAP transport requests for convenient version management
- Supports air-gapped environments with no need for external networks
- Sub-second inference response achievable with HANA acceleration.

## Technical Challenges and Solutions

The project overcame several technical challenges:
1. **ABAP Matrix Operations**: Designed tensor operation classes to implement efficient matrix multiplication, activation functions, etc.
2. **Memory Management**: Controlled model memory to around 250MB via INT8 quantization and shared memory mechanisms.
3. **HANA Acceleration**: Used AMDP to offload compute-intensive operations to HANA, achieving parallel acceleration effects similar to GPUs.

## Application Scenarios and Summary

**Application Scenarios**: Intelligent report generation, ABAP code assistant development, business process optimization, natural language data querying, automatic training document generation, etc.

**Summary**: abap-llm-engine represents an important direction for enterprise AI integration—natively embedding into existing business systems without architecture refactoring. For SAP enterprises, it enables access to cutting-edge AI capabilities within the familiar ABAP environment, providing a feasible path for enterprise AI transformation.

Project URL: https://github.com/cadiraca/abap-llm-engine
License: Apache 2.0