Gastric　cancer　is　a　serious　malignant　tumor.　The　gold　standard　for　diagnosing　gastric　cancer　is　identifying　cancer　cells　using　pathological　slides　under　microscopic　examination.　While　many　approaches　have　been　proposed　for　gastric　cancer　segmentation,　it　is　still　difficult　to　train　large-scale　segmentation　networks　with　scant　gastroscopy　data.　Recently,　Segmentation　Anything　Model　(SAM)　has　received　a　lot　of　interest　lately　for　its　use　in　segmenting　natural　and　medical　images.　However,　due　to　high　computational　complexity　and　huge　computational　costs,　the　application　of　SAM　in　resource　limited　embedded　medical　devices　is　limited.　In　this　paper,　we　proposed　GC-SAM,　a　lightweight　model　for　tumor　segmentation.　The　prompt　encoder　and　mask　decoder　have　been　fine-tuned　to　better　face　the　challenge　of　segmenting　pathological　images　of　gastric　cancer　tissue.　Evaluated　on　an　internal　dataset,　the　GC-SAM　achieved　state-of-the-art　performance　compared　to　classical　image　segmentation　networks,　with　Dice　coefficient　of　0.8186.　In　addition,　external　validation　has　confirmed　its　superior　generalization　ability.　This　study　demonstrates　the　great　potential　of　adapting　GC-SAM　to　pathological　image　segmentation　tasks　in　gastric　cancer　tissue　and　provides　the　possibility　for　deep　learning　image　segmentation　to　be　transferred　to　embedded　medical　devices.　©　2024　IEEE.