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2.3 KiB
2.3 KiB
Qwen3-Next Usage
SGLang has supported Qwen3-Next-80B-A3B-Instruct and Qwen3-Next-80B-A3B-Thinking since this PR.
Launch Qwen3-Next with SGLang
To serve Qwen3-Next models on 4xH100/H200 GPUs:
python3 -m sglang.launch_server --model Qwen/Qwen3-Next-80B-A3B-Instruct --tp 4
Configuration Tips
--max-mamba-cache-size: Adjust--max-mamba-cache-sizeto 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.--mamba-ssm-dtype:bfloat16orfloat32, usebfloat16to save mamba cache size andfloat32to get more accurate results. The default setting isfloat32.--mamba-full-memory-ratio: The ratio of mamba state memory to full kv cache memory. The default is 0.9.
Mamba Radix Cache
SGLang supports prefix caching for Qwen3-Next models named MambaRadixCache, which improves inference speed by reusing computation results. There are two versions of MambaRadixCache:
no_buffer: The default version, which is also other hybrid linear models' choice. When it is enabled, SGLang will automatically close overlap schedule for compatibility reasons.extra_buffer: An optimized version that is compatible with features like page size > 1, overlap schedule, and speculative decoding. It also supports storing mamba state in branching positions. However, it requires two extra mamba spaces for a ping-pong buffer for each request. To enable it, add the argument--mamba-scheduler-strategy extra_bufferwhen launching the server.
EAGLE Speculative Decoding
Description: SGLang has supported Qwen3-Next models with EAGLE speculative decoding.
Usage:
Add arguments --speculative-algorithm, --speculative-num-steps, --speculative-eagle-topk and --speculative-num-draft-tokens to enable this feature. For example:
python3 -m sglang.launch_server \
--model Qwen/Qwen3-Next-80B-A3B-Instruct \
--tp 4 \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--speculative-algo NEXTN
Details can be seen in this PR.