Workarounds for limited hardware environments
For Grok-2's official recommended 8×40GB GPU requirement, hardware adaptation is available through the following programs:
- Quantitative Degradation Program: Trying to adoptfp16maybeint8Quantitative replacement for fp8 (requires modification of SGLang startup parameters)
--quantization), but loses about 15-301 TP3T of modeling accuracy - Model Slicing Techniques: Utilizationpipeline parallelism(Pipeline Parallelism) loads the model into the GPU in stages, reducing the graphics memory requirement by 50%
- CPU offload strategy: ByHugging Face Accelerate(used form a nominal expression)
device_mapfunction that offloads some model layers into system memory
Note: The above programs are subject toSGLangAdjustments in the configuration filemax_total_token_numparameters to control memory usage, it is recommended that you use the--tp 4Reducing tensor parallelism.
This answer comes from the articleGrok-2: xAI's Open Source Hybrid Expert Large Language ModelThe
May not be reproduced without permission:AI productivity tools " How to achieve efficient deployment of Grok-2 models with limited hardware resources?
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