High-Performance AI Inference Acceleration Platform
Beta Inference Acceleration Platform
High-performance LLM inference hub built on proprietary inference chips, supporting multi-model coexistence, canary release and intelligent scheduling, significantly reducing inference costs and latency.
Background
After the explosion of LLM applications, enterprise pressure shifted from 'can we train' to 'can we do large-scale inference at affordable costs'.
High Cost & Energy
Online services have strict requirements for latency and concurrency stability, traditional GPU clusters have high costs and energy consumption, difficult to support large-scale deployment.
Complex Model Deployment & Operations
Lack of engineering capabilities such as multi-model version management, canary release, and fault recovery, putting huge pressure on operations teams.
Solution Content
Building a 'high-performance LLM inference hub' based on proprietary inference chips:
Hardware Layer
Inference chips optimized for Transformer, supporting INT8/FP8/INT4 low-bit precision, ensuring stable accuracy; building inference clusters with high-density servers and high-speed interconnection.
Software Platform
Unified inference service gateway, supporting multi-model coexistence, multi-version canary, A/B testing, rate limiting and circuit breaking; standard API access, abstracting away the complexity of computing scheduling and load balancing.
Operations & Billing
Built-in service monitoring and billing modules, supporting statistics of call volume and resource usage by application/department/tenant, facilitating cost accounting and usage-based billing.
Key Advantages
Let data speak — let results be visible
Inference Speed
Compared to general GPU solutions
Cost Savings
Significantly lower per-inference cost
Service Availability
High availability architecture
Response Latency
Ultimate user experience
Technical Capabilities
Deep technical capabilities powering your business
Dedicated Inference Chips
Deeply optimized for Transformer architecture, supports INT8/FP8 low-precision inference, industry-leading performance-to-power ratio
Elastic Scaling
Auto-scaling based on load, supports multi-model deployment, maximizes resource utilization
Canary Release
Supports A/B testing, traffic distribution, version rollback, ensuring zero-risk model updates
Intelligent Monitoring
Real-time monitoring of QPS, latency, error rate, automatic alerting and failover for service stability
Target Industries
This solution is designed for the following industries and use cases
LLM Platforms
Web Services
Customer Experience (CX) Centers
Content Production
Carriers
Implementation Process
Expert-guided implementation ensuring seamless project delivery
Requirement Assessment
Analyze business scenarios, concurrency requirements, SLA and budget
Architecture Design
Design inference cluster scale, network topology, load balancing strategy
Platform Deployment
Deploy inference cluster, configure service gateway, integrate monitoring
Model Migration
Model format conversion, performance tuning, stress testing
Go Live
Canary release, traffic switching, ops training and support
FAQ
Common questions answered
Supports mainstream LLMs (LLaMA, GPT, ChatGLM), multimodal models (CLIP, Stable Diffusion), traditional NLP models (BERT, T5). Can integrate via standard formats like ONNX, TensorRT, or native PyTorch/TensorFlow models.
Uses multi-replica deployment, automatic failover, rate limiting and circuit breaking. Real-time monitoring of service health, automatic alerting and switching to backup nodes on anomalies. Supports version rollback and one-click takedown of problematic models.
Proprietary inference chips offer high cost-performance, reducing long-term costs by over 50%. Billing based on actual computing usage, avoiding the uncertainty of public cloud token-based pricing. Extremely low marginal costs at scale.
Supports canary by traffic percentage, user groups, geographic location, etc. Can start with 5% traffic to validate new models, gradually expanding after observing normal metrics. Immediate rollback on issues to ensure zero business interruption.
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