lightseekorg--tokenspeed/docs/configuration/compatible-parameters.md

Compatible Parameters

TokenSpeed keeps familiar serving parameter names when the operational meaning is the same. This makes recipes portable while still documenting TokenSpeed-specific behavior explicitly.

Directly Aligned

Parameter	TokenSpeed behavior
positional model	Model path or Hugging Face repo ID.
--model	Equivalent to positional model.
--tokenizer	Tokenizer path.
--tokenizer-mode	Tokenizer implementation mode.
--skip-tokenizer-init	Skip tokenizer initialization.
--load-format	Weight loading format.
--trust-remote-code	Allow custom model code from the model repository.
--dtype	Weight and activation dtype.
--kv-cache-dtype	KV cache storage dtype.
--quantization	Weight quantization method.
--quantization-param-path	KV cache scaling-factor file.
--max-model-len	Maximum sequence length.
--device	Device type. TokenSpeed currently serves CUDA.
--served-model-name	OpenAI-compatible served model name.
--revision	Model revision.
--download-dir	Model download directory.
--hf-overrides	JSON model config overrides.
--host	HTTP bind host.
--port	HTTP bind port.
--api-key	API key for the server.
--chat-template	Chat template name or path.
--gpu-memory-utilization	GPU memory fraction used for weights and KV cache.
--max-num-seqs	Maximum concurrent sequences.
--block-size	KV cache block size.
--enable-prefix-caching	Enable prefix cache reuse.
--no-enable-prefix-caching	Disable prefix cache reuse.
--enforce-eager	Disable CUDA graph execution.
--max-cudagraph-capture-size	Largest CUDA graph capture size.
--tensor-parallel-size, --tp	Set attention tensor parallel size.
--data-parallel-size	Data parallel size.
--enable-expert-parallel	Enable expert parallelism.
--speculative-config	JSON speculative decoding config.
--kv-events-config	JSON KV cache event publisher config; the vLLM-style enable_kv_cache_events field is accepted and defaults to ZMQ when enabled.
--tool-call-parser	OpenAI-compatible tool-call parser.
--reasoning-parser	Reasoning-output parser.

Similar But Not Identical

Recipe parameter	TokenSpeed parameter	Difference
--max-num-batched-tokens	--chunked-prefill-size	TokenSpeed uses this as the scheduler per-iteration issue budget.
--max-num-batched-tokens	--max-total-tokens	TokenSpeed uses this for the global token pool size override.
--tensor-parallel-size, --tp	--attn-tp-size	The familiar alias maps to attention TP. TokenSpeed can split attention, dense, and MoE TP.
--expert-parallel-size	--expert-parallel-size, --ep-size	TokenSpeed supports the familiar name and its existing short form.
--attention-backend	--attention-backend	Name is aligned; available backend values are TokenSpeed-specific.
--moe-backend	--moe-backend	Name is aligned; available backend values are TokenSpeed-specific.

Recipe Translation Notes

• Use tokenspeed serve as the launcher.
• Pass the model path positionally, then keep --trust-remote-code, --max-model-len, --kv-cache-dtype, --gpu-memory-utilization, --max-num-seqs, --tensor-parallel-size, --reasoning-parser, and --tool-call-parser when the model needs them.
• Review --max-num-batched-tokens before copying it. TokenSpeed usually wants --chunked-prefill-size for per-iteration scheduling.
• Review backend names. TokenSpeed backends are optimized for its runtime and kernel packages.
• Keep TokenSpeed-specific --attn-tp-size, --moe-tp-size, --disaggregation-*, and --kvstore-* only when the deployment needs those features.