Technical specifications for the system operating environment
To support model inference at the 32B parameter level, WebThinker requires a specific hardware configuration:
- GPU RequirementsMinimum NVIDIA V100 32GB video memory required, professional computing cards such as A100/A800 recommended.
- Memory requirements: Main memory not less than 64GB, peak consumption up to 48GB during model loading phase
- storage space: 50GB SSD space required for full environment, including model weights and dependency libraries
In actual deployment, the single-task inference latency is about 3-5 seconds/step. For continuous research tasks, it is recommended to configure Kubernetes cluster to realize multi-task concurrency. Notably, the system adopts the vLLM inference framework and supports memory optimization techniques such as PagedAttention, which enables the 32B model to achieve 8-bit quantized operation on consumer-grade graphics cards (e.g., RTX 4090).
This answer comes from the articleWebThinker: An Intelligent Reasoning Tool that Supports Autonomous Web Search and Report WritingThe
May not be reproduced without permission:AI productivity tools " WebThinker's hardware deployment requirements reflect its need for high performance computing
