Large language models (LLMs) are deep learning models trained on massive amounts of text and equipped with tens of billions or even trillions of parameters. By predicting the next token, they can understand, generate, and process natural language to complete diverse tasks such as conversation, summarization, translation, and code generation. By packaging language understanding and generation capabilities into standard cloud service APIs, LLMs enable enterprises and developers to integrate advanced intelligent features without building complex models from scratch, empowering them to focus on business innovation.

In the past, building an application capable of understanding natural language required months of data labeling, specialized model training, and ongoing maintenance. Even then, these models were typically limited to single tasks. They could not generalize well, so any time a new application was needed, those models required massive new investments. As business demands for intelligent interaction, knowledge retrieval, and content production efficiency continue to grow, this siloed development model can no longer keep pace with rapidly changing market requirements.

The emergence of LLMs has completely changed this landscape. Through pre-training on ultra-large-scale corpora, LLMs have acquired rich world knowledge and language patterns, demonstrating powerful emergent capabilities that allow a single model to handle translation, question-answering, writing, reasoning, and other tasks without additional training. This presents a brand-new opportunity for developers: by simply calling a unified interface, they can inject language intelligence into various applications, drastically lowering barriers to entry and accelerating service development. The question is how to make this powerful yet resource-intensive capability stable, user-friendly, and cost-effective so that organizations of all types can reap the benefits. The answer is delivering LLMs as cloud services and continuously optimizing them through technologies such as post-training alignment and Retrieval-Augmented Generation (RAG) to ensure the models are both well-informed and reliable.

• Excellent generalization

  Pre-trained on trillions of tokens of multi-domain data, LLMs can generalize across knowledge domains and task types. A single model simultaneously supports customer service Q&A, document summarization, code generation, creative writing, and other scenarios. There is no need for proprietary models to be trained separately for each task.

• Natural interaction

  LLMs can follow instructions and interact conversationally. You can just describe your needs in natural language to obtain results. This natural language interface abstracts away the complexity of machine commands, significantly boosting usability and enabling non-technical personnel to collaborate with systems easily.

• Continuous evolution and customization

  Through Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Reinforcement Learning from Verifiable Rewards (RLVR), Reinforcement Learning from Human Feedback (RLHF), and prompt engineering, LLMs can rapidly align with domain-specific knowledge and styles while retaining their general capabilities. Enterprises can build intelligent models tailored to their business requirements on top of foundation models at a low cost.

• Scalability and high availability

  Powered by a cloud-native architecture, LLM services automatically scale according to request volume, effortlessly handling peak and off-peak traffic. Technologies such as distributed inference acceleration and model quantization make high-throughput, low-latency real-time interaction possible, ensuring stable business operations.

• Intelligent customer service and knowledge management

  E-commerce platforms and financial institutions can embed LLMs into their customer service systems. Faced with massive daily user inquiries, human agents struggle to respond to all requests in real time, and standard QA databases have limited coverage. By using LLMs to build knowledge-based conversational bots that automatically understand user intent and generate accurate answers in real time using product manuals and policy documents, organizations can drastically shorten response times, boost customer satisfaction, and free human agents from repetitive Q&A to handle high-value tasks.

• Enterprise document and code assistants

  Internal developers and technical writers often face issues such as missing comments, complex logic, and time-consuming document creation when dealing with legacy codebases or technical documentation. Introducing LLM-based code assistants and document generation tools allows teams to generate compliant code snippets from natural language comments or automatically explain and summarize existing code, accelerating development, code review, and knowledge transfer while reducing maintenance costs.

• Multilingual content generation and localization

  Gaming companies and cross-border e-commerce businesses operating globally need to quickly produce large volumes of multilingual marketing copy, product descriptions, and game storylines. Traditional manual translation is slow, expensive, and often suffers from inconsistent stylistic choices. Leveraging the multilingual generation capabilities of LLMs, operations teams can simply provide core details and style guidelines to generate localized content tailored to local cultural habits with a single click, significantly shortening time-to-market.

• Automated data analysis and report generation

  Data analysts in industries like finance and retail need to regularly transform dry structured data into business insights. Creating these reports manually is both time-consuming and prone to inconsistent phrasing. By connecting LLMs to data query engines, users can enter natural language questions, and the model will automatically generate analytical query statements, interpret returned tables, and produce clearly structured reports, allowing decision-makers to grasp the business implications behind the data faster.

• Education & scientific research assistance

  Universities and research institutions often face challenges in massive literature reading and cross-domain knowledge integration during research topic selection, literature reviews, and paper polishing. By using LLMs to summarize literature, brainstorm ideas, and refine drafts, researchers can easily track field developments and organize their thinking, freeing up more time for creative experimental design and theoretical breakthroughs.

The evolution of LLMs can be roughly divided into four stages, with the main trajectory shifting from specialized small models to general-purpose large models, followed by alignment with human values and accelerated engineering deployment.

• Emergence (before 2017)

  Language models were primarily based on statistical language models, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. Models typically had several million to tens of millions of parameters. While achieving preliminary results in tasks like machine translation and speech recognition, they were limited by long-range dependencies and training efficiency, resulting in weak capabilities for complex reasoning and multi-task generalization.

• Establishment of the pre-training paradigm and initial scaling (2017–2019)

  The introduction of the Transformer architecture was a major breakthrough that thoroughly resolved the bottlenecks of parallel computing and long-range dependencies. This shifted the paradigm to pre-training on massive unlabeled text followed by fine-tuning for downstream tasks, with early versions of BERT and GPT serving as representatives. Model parameters scaled to hundreds of millions, significantly enhancing general representation capabilities, though models at this time were primarily designed for single tasks.

• Emergent capabilities at scale (2020–2022)

  Scaling up model parameters and data volume drove the emergence of remarkable new capabilities. This led to the appearance of hundred-billion-parameter models like GPT-3, which demonstrated powerful in-context learning capabilities. These newer models could handle multiple tasks without fine-tuning. Meanwhile, models like Codex extended LLM capabilities into code generation. Models began to be delivered as service platforms.

• Alignment and engineering deployment (2023–present)

  The potential for models to generate harmful, biased, or factually incorrect content during open-domain conversations led to a new focus on model alignment. Technologies like RLHF, RAG, and AI agents were adopted as well. Models could now not only converse but also follow complex instructions, invoke external tools and knowledge bases, and evolve into secure, trustworthy, and customizable AI agents. Simultaneously, open-source and closed-source models developed in parallel, and efficient architectures like Mixture of Experts (MoE) emerged, driving down inference costs and pushing LLMs deep into a vast range of industries.

  During 2024–2025, reasoning models represented by OpenAI's o1/o3 series and DeepSeek-R1 rose to prominence. By investing more compute into the inference phase (generating longer chains of thought and self-verification), they drastically enhanced performance on complex reasoning tasks such as mathematics and coding, marking a paradigm shift from "training-time scaling" to "inference-time scaling."

  In 2026, the deployment of Agentic AI (intelligent agents) became a major trend. Large models transitioned from single-point tool invocations to complex multi-agent collaboration. Companies like OpenAI and Anthropic respectively launched Operator and Computer Use, enabling models to autonomously browse web pages, write code, and operate software. This paved the way for "digital workers" in 2026.

An LLM system typically consists of four key components: the pre-trained foundation model, alignment and post-training module, inference and access gateway, and knowledge enhancement and tool calling. The pre-trained foundation model stores linguistic and world knowledge; the alignment module ensures that the model's output meets human expectations; the inference gateway provides high-throughput and high-availability interfaces; and knowledge enhancement and various tools connect the model to the outside world, addressing limitations in timeliness and factual accuracy.

• Pre-trained foundation model

  This is the core of the LLM, trained through self-supervised learning on billions of or even trillions of tokens of text, code, and other data. It learns grammar, semantics, factual knowledge, and reasoning patterns, manifesting as a deep neural network with massive parameters. The foundation model sets the upper limit of the system's capabilities.

• Alignment and post-training

  This includes steps such as supervised fine-tuning, reward modeling, and reinforcement learning. Learning from high-quality instruction-response samples and human preference comparisons, the model learns to follow instructions, refuse inappropriate requests, and maintain safety. This module "aligns" general capabilities into a specific, safe application format.

• Inference and the access gateway

  This component wraps the model behind a high-concurrency, low-latency inference API. It integrates technologies such as KV Cache management, continuous batching, and model quantization, while supporting traffic scheduling, authentication and authorization, and result caching. The gateway serves as the bridge connecting model capabilities to business applications, ensuring Service Level Agreements (SLAs).

• Retrieval-augmented generation (RAG) and tool calling

  To compensate for the model's knowledge cutoff date and private knowledge blind spots, this module provides RAG and plugin capabilities. By connecting to external vector databases, search engines, or APIs, it dynamically retrieves relevant information to inject into the prompt during task execution, or directly invokes tools like calculators and code interpreters to complete complex multi-step tasks.

These components work together to form a complete closed-loop intelligent service: user requests are injected with context and routed to inference instances through the gateway; during inference, the knowledge enhancement module retrieves external information when necessary; after the model generates a result, the alignment module performs safety reviews; and the final response is returned to the user, while interactions are logged for continuous model optimization.

The basic workflow of an LLM is as follows: natural language input -> tokenization and embedding mapping -> multi-layer Transformer context computation -> probability prediction and token generation -> autoregressive loop and output.

• Natural language input

  The system receives instructions, context, and questions described by the user in natural language or structured prompts via APIs. This can include multi-turn information such as system prompts, historical dialogues, and retrieved knowledge snippets.

• Tokenization and embedding mapping

  The input text is broken down into tokens, the smallest units the model can process. Each token is mapped to a high-dimensional vector via an embedding layer, with positional encoding added simultaneously to inject sequence order information, forming an initial vector representation.

• Multi-layer Transformer context computation

  The vector sequence flows into a deep network composed of tens or even hundreds of Transformer layers. Through the self-attention mechanism, each layer allows a token's representation to integrate information from all other tokens in the sequence, dynamically calculating association strengths between words. Through layer-by-layer abstraction, the model progressively builds a deep understanding of overall sentence semantics, user intent, and knowledge associations.

  The Mixture of Experts (MoE) architecture has become a mainstream architecture for cutting-edge models. MoE transforms "dense computation" into "sparse computation" within traditional neural networks. By using a routing mechanism to dynamically select a small number of experts for computation per token, the model maintains a huge pool of knowledge (large knowledge capacity) while only executing a fraction of those parameters per token (fast speed).

• Probability prediction and token generation

  The final layer outputs a probability distribution covering the entire vocabulary size, predicting the most likely next token. The model samples and selects a token (typically combining strategies like temperature adjustment and Top-p sampling to balance creativity and determinism), appending it to the end of the sequence.

• Autoregressive loop and output

  The newly generated token is appended to the sequence as input for the next round of inference. This process loops until the model outputs a termination symbol or reaches the maximum length. Finally, the generated token sequence is inversely mapped back into a natural language text stream and returned to the user, forming a complete response.

Huawei Cloud has built a comprehensive capability system centered around LLMs, encompassing infrastructure, model services, and industry solutions. This system helps you unleash intelligent productivity securely and efficiently.

• MaaS

  MaaS provides a suite of mainstream large models optimized for Ascend. Serving as an agile hub for enterprises and developers, MaaS eliminates the need for heavy upfront investment in model training, deployment, and operations.

  By providing API access, MaaS delivers flexible and cost-effective solutions for tasks like text generation, intelligent interaction, and data analysis. Models can be called on demand to meet service requirements, reducing technical and time overhead, enabling rapid integration of LLM capabilities, accelerating innovation, and strengthening competitiveness. Quickly call model APIs to experience LLM API calls.

• ModelArts

  Huawei Cloud provides ModelArts, a one-stop AI development platform, to meet the compute requirements for LLM training and inference. ModelArts allows you to perform end-to-end operations such as data cleaning, pre-training, and SFT on a cost-effective, independent compute foundation. You can get started with model training by referring to Creating a Custom Training Job.

• KooSearch and RAG Solutions

  To address the challenges of LLMs, such as limited knowledge timeliness and the need to leverage private knowledge, Huawei Cloud offers KooSearch and Retrieval-Augmented Generation (RAG) solutions. By connecting to internal enterprise documents, databases, and other knowledge sources, these solutions inject precise context during model generation to enable fact-based conversations.

• Security Compliance and Privacy Protection

  Huawei Cloud's large model services comply strictly with privacy protection and regulatory requirements. They incorporate mechanisms such as content moderation and sensitive-word filtering, while regional data centers and resource isolation strategies provide additional safeguards. Together, these measures ensure rigorous protection of your data across transmission, storage, and inference. This allows you to innovate confidently while maintaining enterprise-grade security baselines.