PRODUCTS & SOLUTIONS

SparseLLM

Reshape computation for language intelligence. For developers and enterprise AI teams, start with model quality, then optimize sparse training and deployment for the target environment.

From model structure
to real-world performance.

  • Target model and taskBegin with the base model, task data and quality requirements.
  • Sparse structure and trainingDefine structural constraints and the training approach; inspect weights and model quality.
  • Runtime evaluation and integrationCompare quality, latency, throughput and GPU memory usage using the agreed hardware, numerical precision and workloads.

Each project defines its dense baseline, quality requirements and acceptance criteria, connecting model-level progress to system-level value.

Discuss collaboration

Start with an open-weight model.

The sparse model derived from Qwen3-8B is publicly available. Explore its model card, weights and instructions for running it, then evaluate it on your own tasks.

View the model