Unverified70% confidenceFactTime unknown
对于10万行代码的项目，LLM逐文件理解可能需要数百万Token，而AST解析只需CPU计算，成本接近于零
1
Sources
70%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
CodeGraph Tested: How Code Knowledge Graphs Cut Token Consumption by 90%
bilibiliAGI_Ananas
Related Entities
Related Claims
UnverifiedFor a typical 70-billion-parameter model, the KV cache occupies approximately 0.5 MB of memory per token, and generating 100,000 tokens requires carrying 50 GB of cache.71% similarUnverifiedA single inference at the million-token level requires tens to hundreds of gigabytes of high-end GPU memory (A100/H100 class).67% similarUnverifiedA blank VS Code instance typically consumes 200–400 MB of memory.67% similarUnverifiedRL training workloads have extremely spiky characteristics, potentially requiring 100,000 CPUs suddenly and then dropping back to zero once finished.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/9787API
curl https://kongchang.com/api/v1/knowledge/claims/9787MCP
get_claim(id=9787)