# NUTS speed is very slow for high dimension parameter inference in Turing.jl

**URL:** <https://discourse.julialang.org/t/nuts-speed-is-very-slow-for-high-dimension-parameter-inference-in-turing-jl/80349>\
**Category:** Probabilistic Programming\
**Tags:** turing\
**Created:** [May 2, 2022, 2:03am UTC](https://discourse.julialang.org/t/nuts-speed-is-very-slow-for-high-dimension-parameter-inference-in-turing-jl/80349 "2022-05-02T02:03:20Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![danielw2904](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/danielw2904/32/10890_2.png) [@danielw2904](https://discourse.julialang.org/u/danielw2904)\
**Post date:** [May 2, 2022, 8:11am UTC](https://discourse.julialang.org/t/nuts-speed-is-very-slow-for-high-dimension-parameter-inference-in-turing-jl/80349/3 "2022-05-02T08:11:01Z")

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What about `Turing.setrdcache(true)` in this case?

This worked well for me in a different situation:

> [@Turing.jl for Causal Inference model](https://discourse.julialang.org/t/turing-jl-for-causal-inference-model/75650/19):
>
> Here is my benchmark for two runs of each: Turing.setadbackend(:forwarddiff) Iterations = 1001:1:2000 Number of chains = 4 Samples per chain = 1000 Wall duration = 102.14 seconds Compute duration = 361.99 seconds Iterations = 1001:1:2000 Number of chains = 4 Samples per chain = 1000 Wall duration = 95.9 seconds Compute duration = 333.32 seconds Turing.setadbackend(:tracker) Iterations = 1001:1:2000 Number of chains = 4 Samples per chain = 1000 Wall duration…

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