Inference Framework Overview

The Industry AI Model Platform supports a variety of inference frameworks covering text generation, image generation, text-to-speech, and video generation tasks. This page introduces the core features and use cases of each framework to help you choose the right one for your needs.

Text Generation Frameworks

vLLM

vLLM is a high-performance LLM inference engine that uses PagedAttention to efficiently manage KV Cache memory and significantly improve inference throughput.

Key Features:

• PagedAttention: Paged memory management for attention caches, greatly reducing VRAM waste
• CUDA/HIP Graph Execution: Accelerates inference computation and reduces kernel launch overhead
• Quantization Support: GPTQ, AWQ, INT4, INT8, FP8 quantization formats
• FlashAttention Integration: Speeds up attention computation and reduces VRAM usage
• Multi-Platform: Compatible with NVIDIA, AMD, Intel GPUs and TPUs

Best For: High-throughput, large-scale online inference services.

Links: GitHub | Docs

SGLang

SGLang is an efficient inference framework for LLMs and vision-language models, optimized for structured generation and multimodal inference.

Key Features:

• RadixAttention: Prefix-tree-based attention caching with automatic common prefix reuse
• Jump-Forward Constrained Decoding: Accelerates structured output generation (JSON, regex, etc.)
• Zero-Overhead CPU Scheduling: Eliminates CPU scheduling bottlenecks, maximizing GPU utilization
• Broad Model Support: Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, and more

Best For: Multimodal inference, structured generation, complex prompt engineering.

Links: GitHub | Docs

TGI (Text Generation Inference)

TGI is Hugging Face's production-grade text generation inference server, designed for low-latency, high-reliability scenarios.

Key Features:

• Tensor Parallelism: Multi-GPU distributed inference for very large models
• Token Streaming: Server-Sent Events (SSE) based streaming responses
• Continuous Batching: Dynamically merges request batches to improve GPU utilization
• OpenTelemetry Tracing: Distributed tracing for performance diagnostics
• Prometheus Metrics: Built-in metrics export for monitoring and alerting

Best For: Production low-latency inference services.

Links: GitHub

llama.cpp

llama.cpp is a lightweight inference engine implemented in pure C/C++ that can run LLMs without a GPU.

Key Features:

• Pure C++ Implementation: Highly optimized inference code with minimal resource footprint
• Cross-Platform: Runs on Windows, Linux, and macOS
• Lightweight Deployment: No CUDA or Python environment dependencies required
• GGUF Quantization Format: Multiple quantization precisions (2-bit to 8-bit)

Best For: Resource-constrained environments, local deployment, data privacy-sensitive scenarios.

Links: GitHub

KTransformers

KTransformers is an inference framework designed for real-time conversational scenarios, optimizing multi-turn dialog performance through efficient KV Cache management.

Key Features:

• Efficient KV Cache Management: Optimized context caching for multi-turn conversations
• Multi-Backend Support: CUDA, ROCm, and CPU compute backends
• Low-Latency Optimization: Targeted optimizations for real-time interactive scenarios

Best For: Real-time chatbots, multi-turn dialog applications.

Links: GitHub

MindIE

MindIE is Huawei's Ascend-native inference engine, deeply integrated with the MindSpore ecosystem.

Key Features:

• Ascend Native Support: Deep optimizations for Ascend 910/910B chips
• MindSpore Ecosystem: Seamless integration with Huawei's full-stack AI ecosystem
• Industry-Specific Optimization: Specialized optimizations for autonomous driving, manufacturing, and medical imaging

Best For: Enterprise inference deployments on Huawei Ascend hardware, including autonomous driving, smart manufacturing, and medical imaging.

Links: Docs

Image Generation Frameworks

Hugging Face Inference Toolkit

Hugging Face Inference Toolkit provides auto-optimized inference support for Transformers, Diffusers, and Sentence-Transformers models.

Key Features:

• Auto-Optimized Inference: Automatically detects model type and applies optimal inference configuration
• Diffusers Support: Supports image generation models such as Stable Diffusion
• Sentence-Transformers Support: Efficient inference for embedding models

Best For: Image generation, text embedding, and other Hugging Face ecosystem model inference.

Links: GitHub

Text-to-Speech Frameworks

fishaudio (Fish Speech)

Fish Speech is a high-fidelity speech generation framework with multi-language support.

Key Features:

• High-Fidelity Output: Generates high-quality, natural-sounding speech
• Multi-Language Support: Supports speech synthesis in Chinese, English, and other languages
• Fast Inference: Optimized inference pipeline suitable for real-time speech generation

Best For: Text-to-speech (TTS) applications, intelligent customer service, audiobooks.

Links: GitHub

Video Generation Frameworks

LightX2V

LightX2V is a unified video generation inference framework supporting multiple video generation tasks.

Key Features:

• Unified Task Support: Text-to-Video (T2V), Image-to-Video (I2V), Text-to-Image (T2I), Image-to-Image (I2I)
• 4-Step Distillation: Knowledge distillation reduces inference steps for faster generation
• Quantization Acceleration: Model quantization to lower VRAM usage and inference latency

Best For: Video content creation, short video generation, image/text-to-video workflows.

Links: GitHub

Framework Comparison