Technical solutions for massive code processing
The following strategies can be adopted for analyzing the code base at the GB level:
- Enabling YaRN extensions: Expand context window from 256K to 1M tokens, modify startup parameters
-c 1000000 - slice-and-dice processing technology::
- utilizationqwen split-by-modulesCommand to split code base by functional module
- Summarize the results after analyzing each module individually - Mixed-precision inference: Add at Ollama deployment
--gpu --precision fp16Parameters to reduce video memory usage - Disk Cache Mechanism: Configuration
export QWEN_DISK_CACHE=/path/to/cacheAllow partial intermediate results to be written to disk - tiered loading strategy: By
.gitignorePattern filtering of non-core code such as test files
Recommended Hardware Configuration:
- Handles 1M contexts: A100/A800 graphics cards with at least 80GB of video memory
- 256K context: 24GB video memory RTX 4090 is up to the task
- CPU-only mode: requires 128GB or more of RAM and avx512 instruction set support
This answer comes from the articleQwen3-Coder: open source code generation and intelligent programming assistantThe
May not be reproduced without permission:AI productivity tools " How to overcome memory limitations when analyzing large codebases?
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