This is an example I have tried.
$ R -q -f a.R
> library(rstan)
Loading required package: Rcpp
Loading required package: inline
Loading required package: stats4
rstan (Version 1.1.1, packaged: 2013-02-10 02:59:50 UTC, GitRev: 95e6d90e7aa7)
> sm <- stan_model(file = 'a.stan')
TRANSLATING MODEL 'a' FROM Stan CODE TO C++ CODE NOW.
COMPILING THE C++ CODE FOR MODEL 'a' NOW.
> save('sm', file = 'sm.RData')
>
$ ls -lh sm.RData
-rw-r--r-- 1 jq staff 744K Feb 14 10:28 sm.RData
$ R -q -f b.R
> library(rstan)
Loading required package: Rcpp
Loading required package: inline
Loading required package: stats4
rstan (Version 1.1.1, packaged: 2013-02-10 02:59:50 UTC, GitRev: 95e6d90e7aa7)
> load("sm.RData")
> fit <- sampling(sm, iter = 3, chains = 1)
SAMPLING FOR MODEL 'a' NOW (CHAIN 1).
Iteration: 1 / 3 [ 33%] (Adapting)
Iteration: 2 / 3 [ 66%] (Sampling)
Iteration: 3 / 3 [100%] (Sampling)