## Launch GLM-4.5 / GLM-4.6 with SGLang To serve GLM-4.5 / GLM-4.6 FP8 models on 8xH100/H200 GPUs: ```bash python3 -m sglang.launch_server --model zai-org/GLM-4.6-FP8 --tp 8 ``` ### Configuration Tips - `--max-mamba-cache-size`: Adjust `--max-mamba-cache-size` to increase mamba cache space and max running requests capability. It will decrease KV cache space as a trade-off. You can adjust it according to workload. ### EAGLE Speculative Decoding **Description**: SGLang has supported GLM-4.5 / GLM-4.6 models with [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding). **Usage**: Add arguments `--speculative-algorithm`, `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` to enable this feature. For example: ``` bash python3 -m sglang.launch_server \ --model-path zai-org/GLM-4.6-FP8 \ --tp-size 8 \ --tool-call-parser glm45 \ --reasoning-parser glm45 \ --speculative-algorithm EAGLE \ --speculative-num-steps 3 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 4 \ --mem-fraction-static 0.9 \ --served-model-name glm-4.6-fp8 \ --enable-custom-logit-processor ``` ### Thinking Budget for GLM-4.5 / GLM-4.6 In SGLang, we can implement thinking budget with `CustomLogitProcessor`. Launch a server with `--enable-custom-logit-processor` flag on. Sample Request: ```python import openai from rich.pretty import pprint from sglang.srt.sampling.custom_logit_processor import Glm4MoeThinkingBudgetLogitProcessor client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="*") response = client.chat.completions.create( model="zai-org/GLM-4.6", messages=[ { "role": "user", "content": "Question: Is Paris the Capital of France?", } ], max_tokens=1024, extra_body={ "custom_logit_processor": Glm4MoeThinkingBudgetLogitProcessor().to_str(), "custom_params": { "thinking_budget": 512, }, }, ) pprint(response) ```