# inclusionAI/Ling-2.6-1T — vLLM serving recipe

> Ling-2.6-1T (BailingMoeV2_5) FP8 instruct model with 1T total / 50B active params, hybrid linear + MLA attention, 262K context

Configuration: hardware `h200` (8× H200) · strategy `multi_node_tp` (Multi-Node Tensor Parallel) · 2 nodes · variant `default` (FP8)

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/inclusionAI/Ling-2.6-1T
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/inclusionAI/Ling-2.6-1T.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `strategy`: `single_node_tp`, `multi_node_tp`, `multi_node_tp_pp`, `multi_node_tep`, `multi_node_dep` (multi-node strategies also need `nodes=<n>`)

## Install

Requires vLLM >= 0.20.2.

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

Docker image: `vllm/vllm-openai:latest`

## Serve

Run one command per node: the head (node rank 0) plus 1 worker. Each node's command differs — don't replay one on every node.

### Head (node 0)

```bash
export GLOO_SOCKET_IFNAME=$IFACE_NAME
export NCCL_SOCKET_IFNAME=$IFACE_NAME
vllm serve inclusionAI/Ling-2.6-1T \
  --trust-remote-code \
  --tensor-parallel-size 16 \
  --nnodes 2 \
  --node-rank 0 \
  --master-addr $HEAD_IP
```

### Node 1

```bash
export GLOO_SOCKET_IFNAME=$IFACE_NAME
export NCCL_SOCKET_IFNAME=$IFACE_NAME
vllm serve inclusionAI/Ling-2.6-1T \
  --trust-remote-code \
  --tensor-parallel-size 16 \
  --nnodes 2 \
  --node-rank 1 \
  --master-addr $HEAD_IP \
  --headless
```

Docker equivalent:

```bash
# Head
docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e GLOO_SOCKET_IFNAME=$IFACE_NAME \
  -e NCCL_SOCKET_IFNAME=$IFACE_NAME \
  vllm/vllm-openai:latest inclusionAI/Ling-2.6-1T \
  --trust-remote-code \
  --tensor-parallel-size 16 \
  --nnodes 2 \
  --node-rank 0 \
  --master-addr $HEAD_IP
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

[Ling-2.6-1T](https://huggingface.co/inclusionAI/Ling-2.6-1T) is inclusionAI's
BailingMoeV2_5 FP8 flagship model with 1T total / 50B active parameters, hybrid
linear + MLA attention, and a 262K context window.

### Deployment Configurations

#### Docker (AMD MI300X / MI325X / MI355X, TP=8)

TP=8 has been verified on an MI300X-class node at the model-derived 262K
context. MI325X and MI355X have larger per-GPU HBM.

```bash
docker run --rm -it \
  --cap-add=SYS_PTRACE \
  --ipc=host \
  --privileged=true \
  --shm-size=128GB \
  --network=host \
  --device=/dev/kfd \
  --device=/dev/dri \
  --group-add video \
  -e VLLM_ROCM_USE_AITER=1 \
  vllm/vllm-openai-rocm:v0.28.0 \
    inclusionAI/Ling-2.6-1T \
    --tensor-parallel-size 8 \
    --trust-remote-code
```

### Client Usage

#### Text Generation
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="inclusionAI/Ling-2.6-1T",
    messages=[{"role": "user", "content": "Write a poem about the ocean."}],
    max_tokens=512, temperature=0.7,
)
print(response.choices[0].message.content)
```

### References

- [Ling-2.6-1T on Hugging Face](https://huggingface.co/inclusionAI/Ling-2.6-1T)
- [vLLM ROCm GPU install docs](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/?device=rocm)
- [ROCm vLLM optimization guide](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference-optimization/vllm-optimization.html)