




· Performance is benchmarked against the RTX 4090's inference capabilities
· It is deeply optimized for LLM inference scenarios
· Professional inference performance that surpasses general-purpose GPUs
· Supports LLMs with parameters of 32B or below
· Covering mainstream open source models: Qwen3, Deepseek-R1, etc
· Quantitative model acceleration is natively supported
· Multi-model parallel inference capabilities
· Dedicated LLM inference acceleration architecture
· Optimized Transformer computing unit
· Efficient attention mechanism acceleration
· Intelligent memory management system
Optimized for LLM inference scenarios, the power consumption efficiency is increased by 40%+ compared with general-purpose GPUs, and the inference delay is reduced to millisecond-level response
Support for Hugging Face mainstream model, self-developed efficient inference computing framework, one-click deployment without complex configuration
The 32B model can be deployed on a single card, and the multi-card cascade is nearly linear to speed up, and the power consumption control of professional inference cards is more accurate
The standard PCIe interface is plug-and-play, the complete inference software stack support, and the rich API interfaces and SDKs
·General architecture, inference efficiency is not optimal
·The power consumption is on the high side, and the heat dissipation requirements are strict
·Game card positioning, inference optimization is limited
·Expensive and costly to deploy
·Ecological support is relatively limited
·The development toolchain is complex
·The model adaptation period is long
·The speed of reasoning is severely insufficient
·Large model inference is extremely inefficient
·Latency up to seconds response
·Unable to meet the needs of real-time applications
·Network latency can't be eliminated
·The cost of use continues to grow
·Data privacy security risks
·Strong service dependency
·Dedicated LLM inference acceleration architecture
·The 32B model has complete inference on a single card
·Ultra-low latency for millisecond-level response
·Cost-effective optimized design
·Full ecological toolchain support
·The demand for large language model applications has exploded
·There is a strong demand for privatization deployment of enterprises
·Inference cost control becomes a key requirement
·Real-time AI application scenarios are rapidly expanding
·AI application development enterprises
·Large model service providers
·Research institutes and universities
·Privatized deployment needs enterprises