SiliconFlow

I. Positioning: The “Invisible Engine” of AI Infrastructure

Founded in August 2023, SiliconFlow has positioned itself as a “builder of AI Infrastructure (AI Infra) platforms” from its inception. It focuses on solving three core pain points in large model deployment: low inference efficiency, high computing power costs, and complex hardware adaptation. Unlike companies that directly develop AI applications, SiliconFlow acts more like a “shovel-seller” in the era of large models—by innovating at the intersection of algorithms, systems, and hardware, it builds a critical bridge connecting upper-layer applications to underlying computing power. Its mission, “accelerate the democratization of AGI for humanity,” is gradually being realized by lowering the barriers to AI development and adoption.

II. Technological Barriers: Three Innovations Solving Industry Pain Points

1. Inference Acceleration: A Revolution in Speed and Efficiency

• SiliconLLM Engine: Through deep collaborative optimization of kernels, frameworks, and models, its inference speed is over 10x faster than similar open-source products. It supports seamless scaling across multiple machines and GPUs, and is compatible with mainstream open-source models without additional conversion.

• OneDiff Acceleration Library: Optimized for text-to-image/video scenarios, it boosts compilation speed, reduces memory usage, and supports features like LoRA and ControlNet. It also adapts to inference needs of arbitrary shapes. Tests show that enabling this engine reduces latency for text-to-image tasks by 300%.

2. Heterogeneous Computing Power Management: Breaking Hardware Barriers

Its SiliconCloud platform innovatively enables unified scheduling of computing power across chips from multiple vendors, compatible with mainstream hardware such as NVIDIA, AMD, Huawei Ascend, and Muxi. Through dynamic scaling technology, it multiplies the utilization rate of fragmented computing power, effectively addressing the industry’s long-standing issue of “mismatch between static supply and dynamic demand.” In 2025, the joint launch of Ascend Cloud DeepSeek Inference Service with Huawei Cloud marked a breakthrough—”domestic computing power on par with high-end GPUs.”

3. End-to-End Toolchain: Lowering Development Barriers

It provides one-stop tool support throughout the lifecycle: from model fine-tuning and inference deployment to scenario-based implementation. Enterprise users can upload their own datasets for customized training, while developers can seamlessly switch between 37 mainstream models via a unified API—no need to focus on underlying technical details.

III. Product Matrix: From Cloud Services to Hardware Terminals

1. SiliconCloud: Core Cloud Service Platform

• Multimodal Model Library: Integrates text models (e.g., DeepSeek-R1/V3, Qwen2.5, Llama3), image/video generation models (e.g., Flux.1, SDXL), and covers full scenarios including embedding, speech, and re-ranking.

• Cost-Effective Services: Qwen2-72B is priced at just 4.13 CNY per million tokens; permanently free APIs are available for models with 9B parameters or less; new users receive a free quota of 20 million tokens + 50GB of image generation traffic.

• No-Code Experience: The Playground feature supports testing with 12 industry templates. For text-to-image tasks, parameters like sampling steps and CFG values can be configured with one click; enabling streaming output for conversational models improves creativity by 42%.

2. Enterprise-Grade Solutions

• MaaS Platform: Pre-integrates over 100 open-source/closed-source models, offering end-to-end services for computing power management, training, and deployment. It already serves state-owned enterprises in industries such as power, finance, and manufacturing.

• Private Deployment: Supports hybrid cloud architecture, with a minimum configuration of an 8-GPU A800 cluster. A supporting operation and maintenance monitoring panel enables API call tracing and anomaly diagnosis.

3. Hardware-Collaborative Products

It has launched large-model all-in-one machines and provides edge-cloud collaboration solutions for scenarios like AI mobile terminals and embodied intelligence, effectively addressing latency issues.

IV. Commercial Explosion: Accelerated Funding and Implementation

• Capital Recognition: Completed three rounds of funding within two years of establishment, raising over 600 million CNY in total. Its Series A round was led by Alibaba Cloud, with participation from strategic investors including Meituan and Zhipu AI.

• User Scale: As of mid-2025, the platform has over 7 million individual users and 10,000+ enterprise users, generating over 100 billion tokens daily on average.

• Key Breakthrough: In early 2025, it handled the surging traffic from DeepSeek and became the first third-party platform to support DeepSeek’s deployment on Ascend computing power, significantly boosting its visibility.

V. Ecosystem Implementation: From Developer Tools to Industry Benchmarks

1. Developer Ecosystem

The community has spawned numerous innovative applications: MindSearch (a multi-agent search engine built via API), WeChat AI avatars, Zotero academic paper translation assistants, etc.—covering 9 scenarios including translation, RAG, coding, and image generation. The BizyAir plugin (a ComfyUI cloud node) even allows designers to generate images using cloud computing power without a dedicated GPU.

2. Industry Empowerment

• Smart Government Services: Provides domestic deployment solutions with high throughput and low latency to ensure data security.

• Smart Education: Enables personalized learning path planning through model switching, and improves teaching efficiency via real-time Q&A.

• Industrial Upgrading: Offers customized inference services for manufacturing and energy industries, such as equipment fault diagnosis and energy consumption analysis.

VI. Industry Value: Restructuring AI Computing Power Supply Logic

In the 700 billion CNY AI infrastructure track, SiliconFlow stands out with its positioning as a “professional inference service provider,” forming differentiated competition with players like Fireworks AI and Luchen Technology. Its core value lies in reducing computing power costs by over 60% through technological innovation—allowing individual developers to test top-tier models with free quotas and SMBs to afford large-scale AI deployment. As demonstrated by its collaboration with Huawei Cloud: when domestic computing power can stably support mainstream large models, the cornerstone for the democratization of AGI is truly laid.