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.

Original post →

End-to-End Token Production with Domestic Chips

More from Infra