AirLLM：单卡4GB显存运行700亿大模型，革命性轻量化框架

Introduction

Running large language models (LLMs) has traditionally been a resource-intensive task, often requiring expensive, high-end GPUs with substantial memory. This hardware barrier has limited the accessibility and experimentation with state-of-the-art models for many developers and researchers. AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 emerges as a groundbreaking solution to this challenge. By employing innovative memory optimization techniques, it enables the execution of massive models, such as a 70B parameter model, on a single GPU with as little as 4GB of VRAM. This article provides a comprehensive guide to AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。, covering installation, core concepts, and advanced optimization techniques.

运行大型语言模型（LLM）传统上是一项资源密集型任务，通常需要昂贵的高端GPU和大量显存。这种硬件壁垒限制了许多开发者和研究人员接触和试验最先进模型的机会。AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。作为一项突破性解决方案应运而生。它通过创新的内存优化技术，使得在显存低至4GB的单张GPU上运行大规模模型（例如700亿参数模型）成为可能。本文提供了AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。的完整指南，涵盖安装、核心概念和高级优化技巧。

Quick Start

Environment Setup and Installation

To begin using AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。, ensure your system meets the following prerequisites:

Python 3.8+
PyTorch 1.13+
CUDA 11+ (if using NVIDIA GPU)
Sufficient disk space (the model splitting process requires significant storage)

Install the core AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 package and its dependencies using pip:

pip install airllm
pip install transformers peft accelerate bitsandbytes einops sentencepiece

开始使用AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。前，请确保系统满足以下先决条件：

Python 3.8+

PyTorch 1.13+

CUDA 11+ (如果使用NVIDIA GPU)

充足的磁盘空间（模型拆分过程需要大量存储空间）

使用pip安装AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。核心包及其依赖项：
pip install airllm
pip install transformers peft accelerate bitsandbytes einops sentencepiece

Basic Inference Example

The following code snippet demonstrates the simplest way to perform inference with AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。. The API is designed to be familiar to users of the Hugging Face transformers library.

from airllm import AutoModel

# Initialize the model (supports Hugging Face model IDs or local paths)
model = AutoModel.from_pretrained("garage-bAInd/Platypus2-70B-instruct")

# Prepare input text
input_text = ['What is the capital of the United States?',]

# Tokenization
input_tokens = model.tokenizer(input_text,
    return_tensors="pt",
    return_attention_mask=False,
    truncation=True,
    max_length=128,
    padding=False)

# Generate output
generation_output = model.generate(
    input_tokens['input_ids'].cuda(),
    max_new_tokens=20,
    use_cache=True,
    return_dict_in_generate=True)

# Decode and print the result
output = model.tokenizer.decode(generation_output.sequences[0])
print(output)

以下代码片段展示了使用AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。进行推理的最简单方法。其API设计让熟悉Hugging Face transformers库的用户感到亲切。

from airllm import AutoModel

# 初始化模型（支持Hugging Face模型ID或本地路径）
model = AutoModel.from_pretrained("garage-bAInd/Platypus2-70B-instruct")

# 准备输入文本
input_text = ['What is the capital of the United States?',]

# 分词处理
input_tokens = model.tokenizer(input_text,
    return_tensors="pt",
    return_attention_mask=False,
    truncation=True,
    max_length=128,
    padding=False)

# 生成输出
generation_output = model.generate(
    input_tokens['input_ids'].cuda(),
    max_new_tokens=20,
    use_cache=True,
    return_dict_in_generate=True)

# 解码并输出结果
output = model.tokenizer.decode(generation_output.sequences[0])
print(output)

Core Technical Principles

AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。's ability to run large models on limited hardware is not magic; it's the result of several key technological innovations focused on memory management. The core idea revolves around intelligent model partitioning and streaming. Instead of loading the entire multi-gigabyte model into GPU memory at once, AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 splits the model into layers or smaller shards. During inference, it dynamically loads only the necessary components (e.g., the current layer being computed) into GPU memory, while keeping the rest on the CPU RAM or even disk. This "swapping" mechanism, combined with prefetching and caching strategies, drastically reduces the peak GPU memory footprint.

AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。之所以能在有限的硬件上运行大模型，并非魔法，而是多项专注于内存管理的关键技术创新的成果。其核心思想围绕着智能的模型分区和流式处理。AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。并非一次性将整个数十GB的模型加载到GPU显存中，而是将模型分割成层或更小的分片。在推理过程中，它动态地将仅需要的组件（例如，正在计算的当前层）加载到GPU显存中，而将其余部分保留在CPU内存甚至磁盘上。这种“交换”机制，结合预取和缓存策略，极大地降低了GPU显存的峰值占用。

Technical Feature Comparison

The table below highlights the transformative advantages AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 offers compared to traditional inference methods.

Feature	Traditional Method	AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 Solution
GPU Memory Requirement	80GB+ for a 70B model	As low as 4GB
Inference Speed	Fast	Moderate (with compression optimizations)
Hardware Cost	High	Low
Model Support	Limited	Extensive (supports top-tier models)
Deployment Complexity	Complex	Simple

下表突显了AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。与传统推理方法相比所带来的变革性优势。

特性传统方法 AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。方案

GPU显存需求 运行700亿模型需80GB+ 仅需4GB

推理速度 快适中（具备压缩优化）

硬件成本 高昂低廉

模型支持 有限广泛（支持主流顶级模型）

部署难度 复杂简单

特性	传统方法	AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。方案
GPU显存需求	运行700亿模型需80GB+	仅需4GB
推理速度	快	适中（具备压缩优化）
硬件成本	高昂	低廉
模型支持	有限	广泛（支持主流顶级模型）
部署难度	复杂	简单

Advanced Configuration

AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 provides a rich set of configuration parameters to fine-tune performance for specific use cases and hardware.

1. Model Compression and Acceleration

You can enable 4-bit or 8-bit quantization to significantly reduce model size and accelerate inference, often yielding up to 3x speed improvements.

model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    compression='4bit'  # or '8bit'
)

您可以启用4位或8位量化来显著减小模型大小并加速推理，通常可实现高达3倍的速度提升。
model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    compression='4bit'  # 或 '8bit'
)

2. Performance Profiling Mode

Enable profiling to gain insights into the time consumption of different inference stages, which is invaluable for optimization.

model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    profiling_mode=True  # Outputs detailed timing information
)

启用性能分析模式AirLLM提供的调试功能，可以输出模型推理过程中各阶段的时间消耗信息，帮助用户优化性能。可以深入了解推理不同阶段的时间消耗，这对于优化至关重要。
model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    profiling_mode=True  # 输出详细的时间消耗信息
)

3. Custom Storage Path

Specify a custom directory for saving the model layers/shard files, which is useful for managing disk space or using network storage.

model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    layer_shards_saving_path="/path/to/your/custom/save/directory"
)

指定一个自定义目录来保存模型层/分片文件，这对于管理磁盘空间或使用网络存储非常有用。
model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    layer_shards_saving_path="/path/to/your/custom/save/directory"
)

4. Prefetching Optimization

Prefetching is enabled by default and helps to overlap the loading of upcoming model layers with the computation of current ones, hiding I/O latency and improving throughput.

model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    prefetching=True  # Enabled by default
)

预取功能默认启用，它有助于将后续模型层的加载与当前层的计算重叠进行，从而隐藏I/O延迟并提高吞吐量。
model = AutoModel.from_pretrained(
    "garage-bAInd/Platypus2-70B-instruct",
    prefetching=True  # 默认启用
)

Multi-Model Support

A key strength of AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。 is its broad compatibility with popular open-source LLM architectures. The AutoModel class automatically detects the model type.

ChatGLM Model

from airllm import AutoModel
model = AutoModel.from_pretrained("THUDM/chatglm3-6b-base")
# ... subsequent processing is identical to the Llama2 example

from airllm import AutoModel
model = AutoModel.from_pretrained("THUDM/chatglm3-6b-base")
# ... 后续处理与Llama2示例相同

QWen Model

from airllm import AutoModel
model = AutoModel.from_pretrained("Qwen/Qwen-7B")
# ... subsequent processing

from airllm import AutoModel
model = AutoModel.from_pretrained("Qwen/Qwen-7B")
# ... 后续处理

Other Supported Models

AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。's support extends to numerous other architectures, including but not limited to:

# Baichuan
model = AutoModel.from_pretrained("baichuan-inc/Baichuan2-7B-Base")

# InternLM
model = AutoModel.from_pretrained("internlm/internlm-20b")

# Mistral
model = AutoModel.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

AirLLM一个轻量化的大语言模型推理框架，通过创新的内存优化技术，能够在有限的GPU显存上运行大规模语言模型。的支持扩展到许多其他架构，包括但不限于：
# Baichuan
model = AutoModel.from_pretrained("baichuan-inc/Baichuan2-7B-Base")

# InternLM
model = AutoModel.from_pretrained("internlm/internlm-20b")

# Mistral
model = AutoModel.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

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