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LongCat Flash Chat

NVIDIA
LongCat-Flash-Chat is a 562B-parameter Mixture-of-Experts (MoE) language model optimized for agentic tasks and high-throughput inference. It dynamically activates 18.6-31.3B parameters per token using zero-computation experts and a Shortcut-connected MoE (ScMoE) design. Built on a 28-layer transformer with 64 attention heads and 6,144 hidden size, it supports up to 128K token context. The model employs a multi-stage training pipeline with reasoning-focused pretraining, agentic post-training, and multi-agent task synthesis, enabling advanced reasoning, coding, and iterative interaction capabilities.
TypeMoE LLM
CapabilitiesText Generation, Instruction Following, Reasoning, Mathematical Reasoning+5 more
Links
Paper|HF Model Card
LicenseMIT

Inference Instructions

Deploy and run this model on NVIDIA B200 GPUs using the command below. Copy the command to get started with inference.

CONSOLE
docker run --gpus all 
 --shm-size 128g 
 -p 8000:8000 
 -v ~/.cache/huggingface:/root/.cache/huggingface 
 -e HF_TOKEN='YOUR_HF_TOKEN' 
 --ipc=host 
 lmsysorg/sglang:v0.5.9 
 python3 -m sglang.launch_server 
 --model-path meituan-longcat/LongCat-Flash-Chat 
 --host 0.0.0.0 
 --port 8000 
 --max-prefill-tokens 65536 
 --max-running-requests 1024 
 --attention-backend flashinfer 
 --tp 8 
 --ep 8 
 --mem-fraction-static 0.90 
 --trust-remote-code

Model Benchmarks

Each model was tested with a fixed input size and total token volume while increasing concurrency to measure serving performance under load.

ITL vs Concurrency

Time to First Token

Throughput Scaling

Total Tokens/sec vs Avg TTFT

Vultr Cloud GPU

NVIDIA HGX B200

Deploy NVIDIA B200 on Vultr Cloud GPU

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