lightseekorg--tokenspeed/docs/guides/launching.md

Launching a Server

tokenspeed serve starts an OpenAI-compatible HTTP server. Put the model path directly after the command.

Minimal Launch

tokenspeed serve openai/gpt-oss-20b \
  --host 0.0.0.0 \
  --port 8000 \
  --tensor-parallel-size 1

Production Launch Skeleton

Use explicit parameters in scripts so a deployment is reproducible.

tokenspeed serve nvidia/Kimi-K2.5-NVFP4 \
  --served-model-name kimi-k2.5 \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --max-model-len 262144 \
  --kv-cache-dtype fp8 \
  --quantization nvfp4 \
  --tensor-parallel-size 4 \
  --enable-expert-parallel \
  --chunked-prefill-size 8192 \
  --max-num-seqs 256 \
  --attention-backend trtllm_mla \
  --moe-backend flashinfer_trtllm \
  --reasoning-parser kimi_k25 \
  --tool-call-parser kimik2

Launch Checklist

• Put the model path directly after tokenspeed serve.
• Set --host, --port, and --served-model-name for the API surface.
• Set --max-model-len, --kv-cache-dtype, and --gpu-memory-utilization before tuning concurrency.
• Set --tensor-parallel-size, --data-parallel-size, and --enable-expert-parallel to match the hardware topology.
• Set model-family parsers such as --reasoning-parser and --tool-call-parser when the chat format needs them.
• Set backend choices explicitly for benchmark or production runs.

OpenAI-Compatible Client

from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[{"role": "user", "content": "Write a concise deployment checklist."}],
    max_tokens=256,
)
print(response.choices[0].message.content)

Next

• Model Recipes
• Server Parameters
• Parallelism