Yes, You Can Use TPUs Exclusively for LLM Inference and Agent AI Systems with PyTorch
With PyTorch's robust TPU support via PyTorch/XLA (and recent 2025 enhancements), it's entirely feasible to run LLM inference and AI agent systems solely on TPUs—without needing GPUs. This setup leverages Google Cloud TPUs for cost-efficient, high-throughput serving, especially for large-scale or production workloads. Below, I'll explain the capabilities, practical implementation, and considerations, based on the latest developments as of November 2025.
LLM Inference on TPUs with PyTorch
PyTorch/XLA enables seamless LLM serving on TPUs, with optimizations for low-latency and high-concurrency scenarios. Key enablers include:
- vLLM Integration: vLLM's TPU backend (released October 2025) unifies PyTorch and JAX support, allowing you to serve models like Llama 2/3 or Mixtral with no code changes. It uses a single JAX→XLA lowering path for up to 5x performance gains over early 2025 prototypes, including features like paged attention, flash attention, and prefix caching. Benchmarks show sub-1s time-to-first-token (TTFT) for 70B models on TPU v5e/v6e, with 2–3x better throughput per dollar than equivalent GPUs for batch inference.
- Hugging Face Compatibility: Use the Transformers library directly—PyTorch/XLA hooks into the trainer and pipeline APIs for inference. For example, load a model with pipeline("text-generation", model="meta-llama/Llama-2-7b-hf", device="xla") and run on TPU VMs or pods.
- Performance Examples: On Cloud TPU v5p-256, Mixtral 8x7B achieves global batch sizes of 1024 with bfloat16 precision, delivering ~140 tokens/s for streaming inference. Ironwood (TPU v7, GA November 2025) further boosts this for MoE models common in LLMs.
| Workload | TPU Setup Example | Performance Notes (2025) |
|---|---|---|
| Single-User Chat | v5e-8 (single-host) | TTFT <0.5s; 100+ tokens/s |
| High-Concurrency Serving | v5p-256 pod + vLLM | 2.5x throughput/dollar vs. H100 GPUs |
| Batch Inference | v6e slice + PyTorch/XLA | 3–7x faster prefill/decode phases |
To get started:
- Spin up a TPU VM on Google Cloud: gcloud compute tpus tpu-vm create my-tpu --zone=us-central2-a --accelerator-type=v5e --version=1.15-tpuvm-ubuntu2204.
- Install: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu && pip install torch_xla.
- Run inference: Use vLLM's engine (from vllm import LLM; llm = LLM(model="meta-llama/Llama-3-8B", tensor_parallel_size=8, device="tpu")) for zero-code-change serving.
AI Agent Systems on TPUs
AI agents (e.g., multi-step reasoning systems like ReAct or tool-calling agents built with LangChain/ LlamaIndex) rely on iterative LLM inference, often with dynamic prompts and tool integrations. TPUs handle this well via PyTorch/XLA, as agents are fundamentally inference-heavy:
- Iterative Workloads: Pathways (from Google DeepMind) enables multi-host inference and elastic scaling on TPUs, ideal for agent loops (e.g., query → tool call → response). PyTorch/XLA's SPMD mode partitions agent computations across TPU cores efficiently, supporting variable-length sequences without recompilation overhead after initial JIT.
- Framework Support: Integrate with agent libraries—Hugging Face's transformers for core LLM calls, plus PyTorch for custom logic (e.g., embedding retrieval or decision trees). Recent PyTorch Conference 2025 previews show "native eager mode" for TPUs, minimizing code tweaks for dynamic agent graphs.
- Real-World Use: Deploy via Google Kubernetes Engine (GKE) for resilient agent serving. For example, an RL-aligned agent (using MaxText for post-training) runs inference on Ironwood TPUs with <1s end-to-end latency for complex tasks like code generation or web navigation.
Example agent snippet (using LangChain with PyTorch/XLA):
import torch
import torch_xla.core.xla_model as xm
from langchain.agents import initialize_agent
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
device = xm.xla_device() # TPU device
pipe = pipeline("text-generation", model="meta-llama/Llama-3-8B", device=device)
llm = HuggingFacePipeline(pipeline=pipe)
agent = initialize_agent(tools=[your_tool], llm=llm, agent_type="react")
response = agent.run("Plan a trip to Tokyo using flight APIs.")
Limitations and Best Practices
While fully viable, TPUs aren't a drop-in for every setup:
- Ecosystem Tie-In: Google Cloud-only (no on-prem/multi-cloud like GPUs). Some ops (e.g., certain custom autograd functions) fallback to CPU, causing minor latency spikes—avoid by sticking to XLA-optimized kernels.
- Dynamic Shapes: Agents with highly variable inputs may incur recompilation; use dynamic shape support in PyTorch/XLA 2.5+ to mitigate.
- Cost/Scale: Excels at >10k QPS; for dev/testing, start with free Colab/Kaggle TPUs.
- Migration Tip: If GPU-based, port via Torchax for PyTorch→JAX bridging in vLLM—5x faster than 2025 baselines.
In summary, yes—TPUs via PyTorch/XLA are production-ready for exclusive LLM and agent inference, offering superior efficiency for scale. For prototypes, test on TPU v5e; for prod, scale to pods with vLLM. If your agents need GPU-specific tools (e.g., NVIDIA Dynamo), hybrid is an option, but pure TPU works great for most.
