TPU

SGLang supports high-performance TPU inference through the SGLang-JAX backend, which is specifically optimized for Google Cloud TPUs. The JAX-based implementation delivers exceptional throughput and low latency for Large Language Model (LLM) serving workloads on TPU hardware.

For TPU-specific issues or feature requests, please visit the sglang-jax GitHub issues page.

NOTE: SGLang TPU support is implemented via the SGLang-JAX backend, a dedicated JAX-based inference engine maintained as a separate repository at https://github.com/sgl-project/sglang-jax.

System Requirements

Supported TPU Hardware

TPU Type	HBM Memory	Availability
TPU v6e	32 GB	Google Cloud
TPU v7	96 GB per core	Google Cloud

Software Requirements

• Python: 3.12 or higher
• JAX: Latest version with TPU support
• Environment: Google Cloud TPU VM or compatible TPU runtime
• Optional: SkyPilot for simplified cloud deployment

Feature Support Matrix

SGLang-JAX provides comprehensive TPU-optimized features for production LLM serving:

Feature	Support Status	Description
High-Throughput Continuous Batching	✅	Dynamic request batching for maximum TPU utilization
Radix Tree KV Cache	✅	Memory-efficient prefix sharing between requests
FlashAttention Backend	✅	TPU-optimized attention kernel for long sequences
Tensor Parallelism	✅	Distribute models across multiple TPU cores
Paged Attention	✅	Flexible KV cache management with paging
Speculative Decoding (EAGLE/EAGLE3)	✅	20-40% throughput improvement for compatible models
Chunked Prefill	✅	Mixed prefill-decode batching
OpenAI-Compatible API	✅	Drop-in replacement for OpenAI API
Data Parallel Attention	🚧	In development - Attention computation with data parallelism
Quantization	🚧	In development - Model quantization for reduced memory usage
Multi-LoRA	🚧	In development - Serve multiple LoRA adapters simultaneously

Attention Backend Comparison

Backend	Paged Attention	Spec Decoding	MLA	Sliding Window
FlashAttention (fa)	✅	✅	❌	✅
Native	❌	❌	❌	❌

NOTE: FlashAttention backend is recommended for production workloads due to superior memory efficiency and performance.

Optimized Model List

The following models have been tested and optimized for TPU deployment:

Model Family	Performance Status
Qwen 3	⭐ Recommended for production
Qwen 3 MoE	⭐ Best performance
Qwen 2	Needs improvement
Qwen 2 MoE	Needs improvement
Qwen 1.5	Needs improvement
Llama/LLaMA	Needs improvement
Grok-2	Needs improvement
Gemma 2	Verified on TPU
Bailing MoE	Needs improvement

Installation

Method 1: Using PyPI (Recommended)

pip install sglang-jax

Method 2: From Source

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[all]"

Method 3: Using Docker

NOTE: Docker support for TPU is currently under development. Please use PyPI or source installation methods.

Method 4: Cloud TPU with SkyPilot

SkyPilot provides simplified deployment on Google Cloud TPU:

1. Install SkyPilot and configure GCP access (see SkyPilot documentation)

2. Create a SkyPilot configuration file:

Details

SkyPilot YAML: sglang-jax.sky.yaml

# sglang-jax.sky.yaml
resources:
   accelerators: tpu-v6e-4
   accelerator_args:
      tpu_vm: True
      runtime_version: v2-alpha-tpuv6e

run: |
  git clone https://github.com/sgl-project/sglang-jax.git
  cd sglang-jax
  uv venv --python 3.12
  source .venv/bin/activate
  uv pip install -e "python[all]"

3. Launch your TPU cluster:

# Standard deployment
sky launch -c sglang-jax sglang-jax.sky.yaml --infra=gcp

# With spot instances for cost savings
sky launch -c sglang-jax sglang-jax.sky.yaml --infra=gcp --use-spot

Launch of the Serving Engine

Basic Example: Qwen-7B

JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache python3 -u -m sgl_jax.launch_server \
    --model-path Qwen/Qwen-7B-Chat \
    --trust-remote-code \
    --dist-init-addr=0.0.0.0:10011 \
    --nnodes=1 \
    --tp-size=4 \
    --device=tpu \
    --random-seed=3 \
    --node-rank=0 \
    --mem-fraction-static=0.8 \
    --max-prefill-tokens=8192 \
    --download-dir=/tmp \
    --dtype=bfloat16 \
    --skip-server-warmup \
    --host 0.0.0.0 \
    --port 30000

Key Parameters Explained:

1. JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache - Enables JIT compilation caching to accelerate server startup on subsequent runs
2. --tp-size=4 - Tensor parallelism size; match this to your TPU core count (typically 1, 4, or 8)
3. --device=tpu - Specifies TPU device (this is the default for sglang-jax)
4. --dtype=bfloat16 - Uses bfloat16 precision, which TPUs are optimized for
5. --mem-fraction-static=0.8 - Allocates 80% of TPU HBM for static memory (adjustable from 0.2 to 0.9)
6. --max-prefill-tokens=8192 - Maximum number of tokens processed in the prefill phase

High-Performance Configuration: Qwen3-8B

For production workloads with optimal throughput:

python3 -u -m sgl_jax.launch_server \
    --model-path Qwen/Qwen3-8B \
    --trust-remote-code \
    --tp-size=4 \
    --device=tpu \
    --mem-fraction-static=0.8 \
    --chunked-prefill-size=2048 \
    --dtype=bfloat16 \
    --max-running-requests=256 \
    --page-size=128 \
    --attention-backend=fa

Advanced: Speculative Decoding (EAGLE3)

Speculative decoding can improve throughput by 20-40% for compatible models:

python3 -u -m sgl_jax.launch_server \
    --model-path Qwen/Qwen3-32B \
    --trust-remote-code \
    --device=tpu \
    --tp-size=4 \
    --mem-fraction-static=0.8 \
    --max-prefill-tokens=4096 \
    --attention-backend=fa \
    --dtype=bfloat16 \
    --port=30000 \
    --host=0.0.0.0 \
    --disable-overlap-schedule \
    --speculative-algorithm=EAGLE3 \
    --speculative-draft-model-path=AngelSlim/Qwen3-32B_eagle3 \
    --page-size=64 \
    --speculative-eagle-topk=1 \
    --speculative-num-steps=3 \
    --speculative-num-draft-tokens=4

NOTE: Speculative decoding is currently supported for Qwen3 and LLaMA model families. See the Speculative Decoding documentation for detailed configuration guidance.

Multi-Node Distributed Serving

For large models requiring multiple TPU VMs:

# Node 0 (coordinator)
python3 -m sgl_jax.launch_server \
    --model-path MODEL_PATH \
    --dist-init-addr=NODE0_IP:10011 \
    --nnodes=2 \
    --node-rank=0 \
    --tp-size=8 \
    [other parameters...]

# Node 1 (worker)
python3 -m sgl_jax.launch_server \
    --model-path MODEL_PATH \
    --dist-init-addr=NODE0_IP:10011 \
    --nnodes=2 \
    --node-rank=1 \
    --tp-size=8 \
    [other parameters...]

Benchmarking with Requests

Throughput Testing

Basic throughput benchmark:

python3 -m sgl_jax.bench_serving \
    --backend sgl-jax \
    --dataset-name random \
    --num-prompts=100 \
    --random-input=512 \
    --random-output=128 \
    --max-concurrency=8 \
    --random-range-ratio=1 \
    --warmup-requests=0

Latency Testing

Measure single-batch latency:

python3 -m sgl_jax.bench_one_batch_server \
    --base-url http://127.0.0.1:30000 \
    --model-path Qwen/Qwen-7B-Chat \
    --batch-size=32 \
    --input-len=256 \
    --output-len=32

Comprehensive Benchmark Script

For systematic performance evaluation across different configurations:

#!/bin/bash
set -e

backend=${1:-sgl-jax}
num_prompts_per_concurrency=3
input_seq_lens=(1024 4096 8192)
output_seq_lens=(1 1024)
max_concurrencies=(8 16 32 64 128 256)

for input_seq_len in "${input_seq_lens[@]}"; do
    for output_seq_len in "${output_seq_lens[@]}"; do
        echo "======================================="
        echo "Testing ISL/OSL: $input_seq_len/$output_seq_len"
        echo "======================================="
        for max_concurrency in "${max_concurrencies[@]}"; do
            num_prompts=$((num_prompts_per_concurrency * max_concurrency))
            python3 -m sgl_jax.bench_serving \
                --backend ${backend} \
                --dataset-name random \
                --num-prompts ${num_prompts} \
                --random-input ${input_seq_len} \
                --random-output ${output_seq_len} \
                --max-concurrency ${max_concurrency} \
                --random-range-ratio 1 \
                --disable-ignore-eos \
                --warmup-requests 0
        done
    done
done

For detailed help on all benchmark parameters:

python3 -m sgl_jax.bench_serving --help

See the Benchmark and Profiling Guide for advanced benchmarking techniques and profiling with JAX Profiler.

Performance Optimization

Memory Optimization

Reduce memory usage:

• Lower --mem-fraction-static (from 0.8 → 0.5 → 0.3)
• Decrease --max-prefill-tokens (from 16384 → 8192 → 4096)
• Reduce --max-running-requests

Handle OOM errors:

• Start with conservative memory settings (--mem-fraction-static=0.5)
• Gradually increase until you find the optimal balance
• Increase --page-size for better memory locality (1 → 16 → 64 → 128)

Throughput Optimization

To maximize tokens per second:

• Use FlashAttention backend: --attention-backend=fa
• Enable speculative decoding (EAGLE3) for Qwen3 models (20-40% improvement)
• Increase --max-running-requests to 256+
• Set --mem-fraction-static to 0.8+ (if memory allows)
• Use larger page sizes (64-128)
• Enable chunked prefill: --chunked-prefill-size=2048

Latency Optimization

To minimize time-to-first-token (TTFT) and inter-token latency:

• Reduce --page-size to 1-4
• Lower --max-running-requests (16-32) for smaller batches
• Reduce --chunked-prefill-size
• Use conservative memory settings to avoid GC pauses

TPU-Specific Optimizations

1. JIT Compilation Cache:

   export JAX_COMPILATION_CACHE_DIR=/tmp/jit_cache
   

   Always set this environment variable to cache compiled kernels and accelerate server startup.

2. Data Type Optimization: Use --dtype=bfloat16 for TPU native optimization. TPUs are specifically designed for bfloat16 computations.

3. Tensor Parallelism: Match --tp-size to your TPU core configuration (1, 4, or 8) for optimal model distribution.

4. Attention Backend: Always use --attention-backend=fa (FlashAttention) for production workloads.

Troubleshooting

OOM (Out of Memory) Errors

If you encounter out-of-memory errors:

1. Reduce --mem-fraction-static from 0.8 to 0.5 or lower
2. Decrease --max-prefill-tokens from 8192 to 4096 or 2048
3. Lower --max-running-requests to reduce concurrent batch size
4. Increase --page-size for better memory layout efficiency

Compilation Long-Time

If the server takes too long to start:

1. Ensure JAX_COMPILATION_CACHE_DIR is properly set
2. Understand that the first run requires JIT compilation (this is normal)
3. Subsequent runs will be significantly faster with cached compilations
4. Consider using --skip-server-warmup to defer compilation until first request

Low Throughput

If you’re not achieving expected throughput:

1. Verify --tp-size matches your TPU core configuration
2. Check that --attention-backend=fa is enabled
3. Increase --max-running-requests to enable larger batch formation
4. Consider enabling speculative decoding for compatible models
5. Ensure memory settings allow for sufficient batch sizes

Connection Issues

If clients cannot connect to the server:

1. Ensure --host=0.0.0.0 for external access (not just 127.0.0.1)
2. Verify firewall rules allow traffic on the specified port (default: 30000)
3. Check that the server process is running: curl http://localhost:30000/health

Advanced Features

Speculative Decoding

SGLang-JAX supports EAGLE and EAGLE3 speculative decoding algorithms for Qwen3 and LLaMA model families. Speculative decoding can improve throughput by 20-40% without affecting output quality.

See the Speculative Decoding documentation for detailed configuration and supported model combinations.

Chunked Prefill

Enable mixed prefill-decode batching for better TPU utilization:

--chunked-prefill-size=2048 --enable-mixed-chunk

This allows the scheduler to mix prefill operations with decode operations in the same batch, improving overall throughput.

Custom Attention Backends

SGLang-JAX supports a plugin-based attention backend system. You can implement custom attention kernels optimized for specific use cases.

See the Attention Backend documentation for implementation details.

Environment Verification

Verify your TPU setup before deploying:

python -c "from sgl_jax import check_env; check_env.check_env()"

This command checks:

• Installed package versions
• TPU device availability and specifications
• System resources and configuration
• Compatibility of settings

Contributing

We welcome contributions to improve TPU support in SGLang-JAX!

Areas for Contribution

Check the Development Roadmap to see planned features and find opportunities to contribute new functionality.

Current contribution areas include:

• Performance optimizations for specific TPU generations
• Support for additional model architectures
• Documentation improvements and examples
• Bug reports and fixes
• Benchmark results and performance analysis

How to Contribute

1. Visit the sglang-jax repository
2. Read the Contribution Guide
3. Join the SGL-JAX Slack community for discussions
4. Report issues at sglang-jax/issues

Testing on TPU

For contributors who need TPU access for testing:

• Refer to the TPU Resources Guide for information on accessing TPU hardware
• Use SkyPilot with spot instances for cost-effective testing
• Follow the Benchmark and Profiling Guide for performance validation

References

Documentation

• SGLang-JAX Repository
• SGLang-JAX Installation Guide
• Qwen Models Quick Start
• Benchmark and Profiling Guide
• Speculative Decoding

External Resources

• JAX Documentation
• Google Cloud TPU Documentation
• SkyPilot Documentation