Background　Gastric　cancer　is　a　biologically　aggressive　disease,　accounting　for　a　substantial　proportion　of　cancer-related　deaths　globally.　Accurate　localization　of　the　lesion　by　artificial　intelligence　techniques　helps　timely　and　efficiently　diagnose　and　treat.　Segment　Anything　Model　(SAM)　has　demonstrated　considerable　potential　in　medical　image　segmentation　by　displaying　high　performance　in　numerous　image　benchmark　tests.　However,　its　resource-intensive　nature　limits　feasibility　in　embedded　medical　contexts.Methods　This　study　proposed　GC-SAM,　a　lightweight　model　for　tumor　segmentation.　The　architecture　of　GC-SAM　is　innovatively　proposed,　including　a　knowledge　distillation　image　encoder,　prompt　encoder,　and　mask　decoder,　which　effectively　replaces　the　conventional　fixed　and　computationally　intensive　network　components.Results　Extensive　experiments　demonstrate　that　GC-SAM　significantly　outperforms　both　classical　segmentation　models　and　recent　state-of-the-art　networks.　On　the　internal　test　set,　GC-SAM　achieves　0.8186　Dice　and　0.6504　mIoU,　while　reducing　inference　time　and　parameter　count　by　over　80%　compared　to　the　original　SAM.　On　the　external　dataset,　GC-SAM　maintains　superior　performance　(Dice　0.8350),　demonstrating　excellent　generalization.Conclusions　The　proposed　GC-SAM　model　shows　strong　capability　in　segmenting　gastric　cancer　tissue,　while　also　demonstrating　practical　potential　for　deployment　in　embedded　medical　imaging　devices.