JAX vs vLLM
ComparisonJAX and vLLM occupy fundamentally different positions in the AI model-building toolchain, yet their paths increasingly intersect. JAX is Google's high-performance numerical computing library — the framework behind Gemini and many frontier AI models — built for training, research, and composable mathematical transformations. vLLM is the leading open-source LLM inference and serving engine, built to deploy trained models at scale with maximum throughput and memory efficiency. One trains the models; the other serves them.
What makes this comparison particularly interesting in 2025–2026 is how deeply these tools have converged. vLLM's TPU backend now uses JAX as its lowering path, leveraging JAX's mature XLA primitives to generate optimized computation graphs — even when model definitions are written in PyTorch. This integration has yielded roughly 20% higher throughput on TPU and signals a broader trend: JAX's compiler infrastructure is becoming foundational plumbing for inference engines, not just training frameworks.
Understanding the relationship between JAX and vLLM is essential for teams building production AI systems. The choice isn't either/or — it's about knowing which tool handles which stage of your pipeline, and how they work together to move a model from research breakthrough to production deployment.
Feature Comparison
| Dimension | JAX | vLLM |
|---|---|---|
| Primary purpose | Numerical computing, model training, and research | LLM inference serving and deployment |
| Pipeline stage | Model development and training | Model serving and production inference |
| Core innovation | Composable transformations (jit, grad, vmap, pmap) with XLA compilation | PagedAttention for memory-efficient KV cache management (<4% memory waste vs 60–80% in naive approaches) |
| Hardware support | GPU (NVIDIA, AMD), Google TPUs, CPU | NVIDIA GPU, AMD ROCm (first-class since 2025), Intel XPU, Google TPU (via JAX backend), CPU |
| Programming model | Functional, pure-function transformations with NumPy-like API | Declarative configuration with OpenAI-compatible API server |
| Notable users | Google DeepMind (Gemini 3), Anthropic, xAI, Apple | Cloud providers, AI startups, enterprise deployments globally |
| Scaling approach | pmap/pjit for data and model parallelism across TPU/GPU pods | Tensor parallelism, pipeline parallelism, disaggregated prefill/decode |
| Performance focus | Training throughput, gradient computation, XLA optimization | Inference throughput (up to 24x over HuggingFace Transformers), latency (lowest p50/p95 TTFT) |
| Current version (Mar 2026) | v0.9.2 (March 2026) with Shardy partitioner default | V1 engine default since 2025, Semantic Router v0.1 (Iris) released Jan 2026 |
| Ecosystem integration | Flax, Optax, Orbax, MaxText; XLA compiler shared with TensorFlow and PyTorch/XLA | OpenAI-compatible API, integrates with JAX and PyTorch model definitions, works with quantization frameworks (FP8, FP4) |
| Learning curve | Steep — requires understanding functional programming, pure functions, and XLA compilation model | Moderate — straightforward deployment API, but tuning for optimal throughput requires systems knowledge |
Detailed Analysis
Different Stages, Shared Infrastructure
JAX and vLLM address fundamentally different stages of the AI lifecycle. JAX is where models are born — its composable transformations (jit, grad, vmap, pmap) make it the preferred environment for researchers designing novel architectures and training them at scale. Google trained every version of Gemini, including Gemini 3, entirely on JAX running across TPU pods. Meanwhile, vLLM is where trained models go to work — its PagedAttention algorithm and optimized serving engine handle the mechanics of turning a static model checkpoint into a responsive, cost-efficient inference endpoint.
The most significant development in their relationship came in 2025, when vLLM's TPU backend adopted JAX as its compilation path. Rather than using PyTorch/XLA directly, vLLM now routes through JAX's more mature primitives to generate HLO graphs for XLA compilation. This architectural choice delivered ~20% throughput improvement and signals JAX's growing role as compiler infrastructure for the entire ML stack, not just a training framework.
Training vs. Inference: The Core Divide
JAX excels at workloads where you need automatic differentiation, custom gradient computation, and fine-grained control over parallelism strategies. Its functional programming model ensures that transformations compose cleanly — you can JIT-compile a function, vectorize it across a batch, and distribute it across devices, all without changing the core logic. This composability is why research teams building frontier models gravitate toward JAX.
vLLM excels at the entirely different challenge of serving a pre-trained model to thousands of concurrent users. Its V1 engine (default since 2025) uses zero-copy DMA transfers and disaggregated prefill/decode scheduling to minimize latency. In 2026 H100 benchmarks, vLLM demonstrated the highest throughput at every concurrency level compared to TensorRT-LLM and SGLang, with the lowest p50 and p95 time-to-first-token.
Hardware Strategy and Ecosystem
JAX's hardware story is tightly coupled with Google's TPU ecosystem, though it supports NVIDIA and AMD GPUs as well. The AI Hypercomputer architecture — combining TPU hardware, Jupiter networking, and JAX/XLA software — represents Google's vertically integrated approach to AI infrastructure. JAX is the software layer that makes TPU pods programmable at scale.
vLLM has pursued a broader hardware strategy. In 2025, AMD ROCm became a first-class platform with FP8/FP4 quantization and optimized KV cache performance. Intel XPU gained CUDA graph support and GPU-direct RDMA. This hardware breadth makes vLLM the more portable choice for inference, particularly for organizations not locked into a single cloud provider's accelerator ecosystem.
The Ecosystem and Developer Experience
JAX's ecosystem includes Flax for neural network layers, Optax for optimizers, and Orbax for checkpointing. Google DeepMind's MaxText provides a reference implementation for scalable LLM training on JAX. However, JAX's functional programming model presents a genuine learning curve — mutable state is forbidden, and understanding how XLA compilation interacts with Python control flow requires significant investment.
vLLM offers a more accessible developer experience for its target use case. An OpenAI-compatible API server can be stood up with a single command, and the library handles the complex systems engineering of batching, memory management, and scheduling automatically. The January 2026 release of Semantic Router v0.1 (Iris) added intelligent request routing, further simplifying multi-model deployments for AI agent architectures.
Production Economics and Scale
For organizations operating at scale, the economic implications of choosing the right tool for each pipeline stage are substantial. JAX's XLA compilation produces highly optimized training kernels that minimize wasted compute during the most expensive phase of AI development. Its efficient use of TPU interconnects via pjit sharding can reduce training costs significantly compared to less hardware-aware frameworks.
On the inference side, vLLM's PagedAttention reduces GPU memory waste from 60–80% to under 4%, meaning each GPU serves dramatically more concurrent requests. For companies powering agentic AI applications where every model call carries a cost, vLLM's throughput advantages translate directly to lower per-query economics. The V1 engine's disaggregated prefill/decode further prevents long-context prompts from blocking shorter requests, improving tail latency in production.
Best For
Training a frontier LLM from scratch
JAXJAX's composable transformations, XLA optimization, and TPU pod scaling make it the proven choice — Gemini 3 was trained entirely on JAX. vLLM has no training capabilities.
Serving an LLM API to production users
vLLMvLLM's PagedAttention, V1 engine, and OpenAI-compatible API deliver the highest throughput and lowest latency for production inference. JAX is not designed for serving.
Research prototyping with custom architectures
JAXJAX's functional model and grad/vmap/pmap composability let researchers iterate on novel architectures without fighting the framework. vLLM only serves existing architectures.
Deploying an agentic AI system at scale
vLLMAgent architectures require high-throughput, low-latency model serving with efficient concurrent request handling — exactly what vLLM's scheduling and memory management optimize for.
Running inference on Google TPUs
BothvLLM's TPU backend now uses JAX as its lowering path, meaning both tools are involved. Use vLLM for serving with JAX powering the compilation underneath.
Fine-tuning models with custom loss functions
JAXJAX's automatic differentiation and JIT compilation of custom gradient computations make it ideal for fine-tuning with non-standard objectives. vLLM does not support training.
Multi-model inference routing
vLLMvLLM's Semantic Router (Iris, released Jan 2026) provides intelligent request routing across multiple models — a capability JAX doesn't address.
Building a complete model-to-deployment pipeline
BothUse JAX for training and vLLM for serving. They are complementary tools — vLLM even uses JAX internally for TPU compilation. A mature pipeline uses both.
The Bottom Line
JAX and vLLM are not competitors — they are complementary tools that address opposite ends of the AI pipeline. JAX is the premier framework for training and research, particularly within Google's TPU ecosystem. vLLM is the leading engine for production LLM inference. The fact that vLLM now uses JAX internally as its TPU compilation path underscores how these tools work together rather than against each other.
If you are building or training models, JAX is the right choice — especially if you need the mathematical composability and hardware efficiency that have made it the framework behind Gemini, and a favorite of Anthropic, xAI, and Apple. If you are deploying trained models to serve real users, vLLM is the clear leader, with the best throughput benchmarks on H100s in 2026, broad hardware support across NVIDIA, AMD, and Intel, and production-ready features like disaggregated prefill/decode and semantic routing.
For most organizations building AI products, the practical answer is: use both. Train or fine-tune on JAX (or PyTorch), then deploy with vLLM. The two tools have converged at the infrastructure level — and teams that understand how to leverage each at the right pipeline stage will ship faster and spend less on compute than those trying to force one tool to do everything.