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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.

High-Performance AI Inference Acceleration Platform

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

3x
Inference Speed

Compared to general GPU solutions

50%
Cost Savings

Significantly lower per-inference cost

99.99%
Service Availability

High availability architecture

ms-level
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

01
1 week
Requirement Assessment

Analyze business scenarios, concurrency requirements, SLA and budget

02
1-2 weeks
Architecture Design

Design inference cluster scale, network topology, load balancing strategy

03
2-3 weeks
Platform Deployment

Deploy inference cluster, configure service gateway, integrate monitoring

04
2 weeks
Model Migration

Model format conversion, performance tuning, stress testing

05
1 week
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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