G2

Alexis V.

Developer & Data Analyst

Computer Software

Small-Business (50 or fewer emp.)

5/30/2026

"Seamless GPU/TPU Acceleration for Faster Python and NumPy Workloads"

5/5

What do you like best about Jax?

What I like most about JAX is how seamlessly it lets me run Python and NumPy code directly on GPUs and TPUs. The execution speed is excellent, and it makes training and scaling large machine learning models noticeably faster. Review collected by and hosted on G2.com.

What do you dislike about Jax?

The strict requirement to stick to functional programming can make the overall code structure feel quite cumbersome. Constantly having to manually pass around and split explicit Pseudo-Random Number Generator (PRNG) keys becomes tedious, and it can bloat the codebase very quickly. Review collected by and hosted on G2.com.

What problems is Jax solving and how is that benefiting you?

JAX addresses key limitations of standard NumPy, which is largely CPU-bound and doesn’t offer native automatic differentiation. For my workflow, the biggest advantage is being able to run my custom numerical computations and deep learning models smoothly on GPUs or TPUs, leading to major training speedups without forcing me to rewrite everything in a completely different ecosystem. Review collected by and hosted on G2.com.