End-to-End Token Production with Domestic Chips
智东西 · wechat · 2026-07-17
This in-depth interview discusses how Qingcheng Jizhi uses a "front shop, back factory" approach to chain domestic chips, inference frameworks, and large models into a deployable Token production pipeline.
Core Thesis
- The bottleneck for domestic AI adoption isn't just chips and models; a missing inference engine is needed to convert compute into Tokens.
- Overseas inference engines are tailored for the Nvidia ecosystem. Migrating them directly to domestic chips causes issues with FP4/FP8 adaptation, VRAM usage, and precision loss.
What the Chitu Inference Engine Does
- Developed a software-based floating-point simulation architecture to preserve native model precision as much as possible.
- Optimizes mixed quantization, KV Cache scheduling, and multi-card parallelism based on domestic chip characteristics.
- Aims to enable domestic cards to produce Tokens more efficiently without requiring massive hardware overhauls.
Quantified Gains
- The article notes that deploying the full DeepSeek model using traditional open-source inference frameworks might require 4 domestic 8-card servers.
- Integrating Chitu reduces this to a single server, cutting overall hardware costs by roughly 75%.
AIPing and Bagua Furnace
- AIPing is a one-stop Token scheduling and service platform, unifying access to 30+ providers and 600+ model services.
- The Bagua Furnace (八卦炉) series targets Agent building, local private deployment, and compute center integration.
- The article emphasizes that their value lies in the flywheel of "evaluation—routing—call data," which continuously improves stability and success rates.
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