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+# Serve Qwen3-MoE with SGLang-Jax on TPU
+
+SGLang-Jax supports multiple Mixture-of-Experts (MoE) models from the Qwen3 family with varying hardware requirements:
+
+- **[Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)**: Runs on 4 TPU v6e chips
+- **[Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct)**: Requires 64 TPU v6e chips (16 nodes × 4 chips)
+- Other Qwen3 MoE variants with different scale requirements
+
+**This tutorial focuses on deploying Qwen3-Coder-480B**, the largest model requiring a multi-node distributed setup. For smaller models like Qwen3-30B, you can follow similar steps but with adjusted node counts and parallelism settings.
+
+## Hardware Requirements
+
+Running Qwen3-Coder-480B requires a multi-node TPU cluster:
+
+- **Total nodes**: 16
+- **TPU chips per node**: 4 (v6e)
+- **Total TPU chips**: 64
+- **Tensor Parallelism (TP)**: 32 (for non-MoE layers)
+- **Expert Tensor Parallelism (ETP)**: 64 (for MoE experts)
+
+
+## Installation
+
+### Option 1: Install from PyPI
+
+```bash
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install sglang-jax
+```
+
+### Option 2: Install from Source
+
+```bash
+git clone https://github.com/sgl-project/sglang-jax
+cd sglang-jax
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install -e python/
+```
+## Launch Distributed Server
+
+### Preparation
+
+1. **Get Node 0 IP address** (coordinator):
+
+```bash
+# On node 0
+hostname -I | awk '{print $1}'
+```
+
+Save this IP as `NODE_RANK_0_IP`.
+
+2. **Download model** (recommended to use shared storage or pre-download on all nodes):
+
+```bash
+export HF_TOKEN=your_huggingface_token
+huggingface-cli download Qwen/Qwen3-Coder-480B --local-dir /path/to/model
+```
+
+### Launch Command
+
+Run the following command **on each node**, replacing:
+- `<NODE_RANK_0_IP>`: IP address of node 0
+- `<NODE_RANK>`: Current node rank (0-15)
+- `<QWEN3_CODER_480B_MODEL_PATH>`: Path to the downloaded model
+
+```bash
+JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache \
+python3 -u -m sgl_jax.launch_server \
+    --model-path <QWEN3_CODER_480B_MODEL_PATH> \
+    --trust-remote-code \
+    --dist-init-addr=<NODE_RANK_0_IP>:10011 \
+    --nnodes=16 \
+    --tp-size=32 \
+    --device=tpu \
+    --random-seed=3 \
+    --mem-fraction-static=0.8 \
+    --chunked-prefill-size=2048 \
+    --download-dir=/dev/shm \
+    --dtype=bfloat16 \
+    --max-running-requests=128 \
+    --skip-server-warmup \
+    --page-size=128 \
+    --tool-call-parser=qwen3_coder \
+    --node-rank=<NODE_RANK>
+```
+
+### Example for Specific Nodes
+
+**Node 0 (coordinator):**
+
+```bash
+JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache \
+python3 -u -m sgl_jax.launch_server \
+    --model-path /path/to/Qwen3-Coder-480B \
+    --trust-remote-code \
+    --dist-init-addr=10.0.0.2:10011 \
+    --nnodes=16 \
+    --tp-size=32 \
+    --device=tpu \
+    --random-seed=3 \
+    --mem-fraction-static=0.8 \
+    --chunked-prefill-size=2048 \
+    --download-dir=/dev/shm \
+    --dtype=bfloat16 \
+    --max-running-requests=128 \
+    --skip-server-warmup \
+    --page-size=128 \
+    --tool-call-parser=qwen3_coder \
+    --node-rank=0
+```
+
+**Node 1:**
+
+```bash
+# Same command but with --node-rank=1
+JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache \
+python3 -u -m sgl_jax.launch_server \
+    --model-path /path/to/Qwen3-Coder-480B \
+    --trust-remote-code \
+    --dist-init-addr=10.0.0.2:10011 \
+    --nnodes=16 \
+    --tp-size=32 \
+    --device=tpu \
+    --random-seed=3 \
+    --mem-fraction-static=0.8 \
+    --chunked-prefill-size=2048 \
+    --download-dir=/dev/shm \
+    --dtype=bfloat16 \
+    --max-running-requests=128 \
+    --skip-server-warmup \
+    --page-size=128 \
+    --tool-call-parser=qwen3_coder \
+    --node-rank=1
+```
+
+Repeat for all 16 nodes, incrementing `--node-rank` from 0 to 15.
+
+## Configuration Parameters
+
+### Distributed Training
+
+- `--nnodes`: Number of nodes in the cluster (16)
+- `--node-rank`: Rank of the current node (0-15)
+- `--dist-init-addr`: Address of the coordinator node (node 0) with port
+
+### Model Parallelism
+
+- `--tp-size`: Tensor parallelism size for non-MoE layers (32)
+- **ETP**: Expert tensor parallelism automatically configured to 64 based on total chips
+
+### Memory and Performance
+
+- `--mem-fraction-static`: Memory allocation for static buffers (0.8)
+- `--chunked-prefill-size`: Prefill chunk size for batching (2048)
+- `--max-running-requests`: Maximum concurrent requests (128)
+- `--page-size`: Page size for memory management (128)
+
+### Model-Specific
+
+- `--tool-call-parser`: Parser for tool calls, set to `qwen3_coder` for this model
+- `--dtype`: Data type for inference (bfloat16)
+- `--random-seed`: Random seed for reproducibility (3)
+
+## Verification
+
+Once all nodes are running, the server will be accessible via the coordinator node (node 0). You can test it with:
+
+```bash
+curl http://<NODE_RANK_0_IP>:8000/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Qwen/Qwen3-Coder-480B",
+        "prompt": "def fibonacci(n):",
+        "max_tokens": 200,
+        "temperature": 0
+    }'
+```
diff --git a/inference/trillium/SGLang-Jax/Qwen3/README.md b/inference/trillium/SGLang-Jax/Qwen3/README.md
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+# Serve Qwen3 with SGLang-Jax on TPU
+
+This guide demonstrates how to serve [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) and [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) using SGLang-Jax on TPU.
+
+
+## Provision TPU Resources
+
+For **Qwen3-8B**, a single v6e chip is sufficient. For **Qwen3-32B**, use 4 chips or more.
+
+### Option 1: Using gcloud CLI
+
+Install and configure gcloud CLI by following the [official installation guide](https://cloud.google.com/sdk/docs/install).
+
+**Create TPU VM:**
+
+```bash
+gcloud compute tpus tpu-vm create sgl-jax \
+    --zone=us-east5-a \
+    --version=v2-alpha-tpuv6e \
+    --accelerator-type=v6e-4
+```
+
+**Connect to TPU VM:**
+
+```bash
+gcloud compute tpus tpu-vm ssh sgl-jax --zone us-east5-a
+```
+
+### Option 2: Using SkyPilot (Recommended for Development)
+
+SkyPilot simplifies TPU provisioning and offers automatic cost optimization, instance management, and environment setup.
+
+**Prerequisites:**
+- [Install SkyPilot](https://docs.skypilot.co/en/latest/getting-started/installation.html)
+- [Configure GCP credentials](https://docs.skypilot.co/en/latest/getting-started/installation.html#gcp)
+
+**Create configuration file `sgl-jax.yaml`:**
+
+```yaml
+resources:
+  accelerators: tpuv6e-4
+  accelerator_args:
+    tpu_vm: True
+    runtime_version: v2-alpha-tpuv6e
+
+setup: |
+  uv venv --python 3.12
+  source .venv/bin/activate
+  uv pip install sglang-jax
+```
+
+**Launch TPU cluster:**
+
+```bash
+sky launch sgl-jax.yaml \
+    --cluster=sgl-jax-skypilot-v6e-4 \
+    --infra=gcp \
+    -i 30 \
+    --down \
+    -y \
+    --use-spot
+```
+
+This command will:
+- Find the lowest-cost spot instance across regions
+- Automatically shut down after 30 minutes of idleness
+- Set up the SGLang-Jax environment automatically
+
+**Connect to cluster:**
+
+```bash
+ssh sgl-jax-skypilot-v6e-4
+```
+
+> **Note:** SkyPilot manages the external IP automatically, so you don't need to track it manually.
+
+## Installation
+
+> **Note:** If you used SkyPilot to provision resources, the environment is already set up. Skip to the [Launch Server](#launch-server) section.
+
+For gcloud CLI users, install SGLang-Jax using one of the following methods:
+
+### Option 1: Install from PyPI
+
+```bash
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install sglang-jax
+```
+
+### Option 2: Install from Source
+
+```bash
+git clone https://github.com/sgl-project/sglang-jax
+cd sglang-jax
+uv venv --python 3.12 && source .venv/bin/activate
+uv pip install -e python/
+```
+
+## Launch Server
+
+Set the model name and start the SGLang-Jax server:
+
+```bash
+export MODEL_NAME="Qwen/Qwen3-8B"  # or "Qwen/Qwen3-32B"
+
+JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache \
+uv run python -u -m sgl_jax.launch_server \
+    --model-path ${MODEL_NAME} \
+    --trust-remote-code \
+    --tp-size=4 \
+    --device=tpu \
+    --mem-fraction-static=0.8 \
+    --chunked-prefill-size=2048 \
+    --download-dir=/tmp \
+    --dtype=bfloat16 \
+    --max-running-requests 256 \
+    --skip-server-warmup \
+    --page-size=128
+```
+
+### Configuration Parameters
+
+- `--tp-size`: Tensor parallelism size, should equal the number of TPU chips in your instance
+- `--mem-fraction-static`: Fraction of memory allocated for static buffers
+- `--chunked-prefill-size`: Size of prefill chunks for batching
+- `--max-running-requests`: Maximum number of concurrent requests
+
+## Run Benchmark
+
+Test serving performance with different workload configurations:
+
+```bash
+uv run python -m sgl_jax.bench_serving \
+    --backend sgl-jax \
+    --dataset-name random \
+    --num-prompts 256 \
+    --random-input 4096 \
+    --random-output 1024 \
+    --max-concurrency 64 \
+    --random-range-ratio 1 \
+    --warmup-requests 0
+```
+
+### Benchmark Parameters
+
+- `--backend`: Backend engine (use `sgl-jax`)
+- `--random-input`: Input sequence length (e.g., 1024, 4096, 8192)
+- `--random-output`: Output sequence length (e.g., 1, 1024)
+- `--max-concurrency`: Maximum number of concurrent requests (e.g., 8, 16, 32, 64, 128, 256)
+- `--num-prompts`: Total number of prompts to send
+
+You can test various combinations of input/output lengths and concurrency levels to evaluate throughput and latency characteristics.
diff --git a/inference/trillium/SGLang-Jax/README.md b/inference/trillium/SGLang-Jax/README.md
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+# Serve SGLang-Jax on Trillium TPUs (v6e)
+
+This repository provides examples demonstrating how to deploy and serve SGLang-Jax on Trillium TPUs using GCE (Google Compute Engine) for a select set of models.
+
+- [Qwen3-8B/32B](./Qwen3/README.md)
+- [Qwen/Qwen3-30B-A3B/Qwen/Qwen3-Coder-480B-A35B-Instruct](./Qwen3-MoE/README.md)
+
+The SGLang-Jax project continues to support new models. For the specific model list, see https://github.com/sgl-project/sglang-jax/tree/main/python/sgl_jax/srt/models.
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