inclusionAI/Ling-3.1-flash

Overview

inclusionAI/Ling-3.1-flash uses the BailingMoeV3ForCausalLM architecture with a hybrid MLA/KDA attention stack, 72 layers, 512 routed experts (8 active per token), and one shared expert. The checkpoint uses serialized block-FP8 E4M3 weights with 128x128 block scaling and dynamic activation quantization. Non-quantized modules use BF16.

Prerequisites

• vLLM: nightly build
• Deployment: a single node with 8 GPUs, using TP8 + EP8
• Precision: serialized block FP8 weights with BF16 compute
• Context length: 262,144 tokens
• Memory: approximately 689 GB aggregate VRAM as a loading estimate; allow additional capacity for KV cache and the intended workload

Launching the Server

vllm serve inclusionAI/Ling-3.1-flash \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-expert-parallel \
  --gpu-memory-utilization 0.9 \
  --enable-chunked-prefill \
  --compilation-config '{"cudagraph_mode":"FULL_AND_PIECEWISE"}' \
  --enable-prefix-caching \
  --enable-auto-tool-choice \
  --tool-call-parser ling3 \
  --reasoning-parser ling3

Thinking Mode

Thinking is selected per request through the chat template rather than by a server flag:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="inclusionAI/Ling-3.1-flash",
    messages=[{"role": "user", "content": "Solve the problem step by step."}],
    temperature=0.0,
    max_tokens=16384,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
msg = response.choices[0].message
print(msg.reasoning)
print(msg.content)

--reasoning-parser ling3 separates thinking traces from the final answer. Keep the prompt and requested output within the configured context limit.

References

• Ling-3.1-flash on Hugging Face
• vLLM nightly installation