diff --git a/candle-examples/examples/mimi/main.rs b/candle-examples/examples/mimi/main.rs
index cfc1a553..788b3fd9 100644
--- a/candle-examples/examples/mimi/main.rs
+++ b/candle-examples/examples/mimi/main.rs
@@ -39,6 +39,11 @@ struct Args {
     /// The model weight file, in safetensor format.
     #[arg(long)]
     model: Option<String>,
+
+    /// Whether to use streaming or not, when streaming slices of data of the given size are passed
+    /// to the encoder/decoder one at a time.
+    #[arg(long)]
+    streaming: Option<usize>,
 }
 
 fn main() -> Result<()> {
@@ -87,20 +92,46 @@ fn main() -> Result<()> {
                     pcm
                 }
             };
-            let pcm_len = pcm.len();
-            let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
-            println!("input pcm shape: {:?}", pcm.shape());
-            model.encode(&pcm)?
+            match args.streaming {
+                Some(chunk_size) => {
+                    let mut code_chunks = vec![];
+                    for pcm in pcm.chunks(chunk_size) {
+                        let pcm = Tensor::new(pcm, &device)?.reshape((1, 1, ()))?;
+                        let code_chunk = model.encode(&pcm)?;
+                        code_chunks.push(code_chunk)
+                    }
+                    Tensor::cat(&code_chunks, candle::D::Minus1)?
+                }
+                None => {
+                    let pcm_len = pcm.len();
+                    let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
+                    println!("input pcm shape: {:?}", pcm.shape());
+                    model.encode(&pcm)?
+                }
+            }
         }
     };
     println!("codes shape: {:?}", codes.shape());
+    model.reset_state();
 
     match args.action {
         Action::AudioToCode => {
             codes.save_safetensors("codes", &args.out_file)?;
         }
         Action::AudioToAudio | Action::CodeToAudio => {
-            let pcm = model.decode(&codes)?;
+            let pcm = match args.streaming {
+                Some(chunk_size) => {
+                    let seq_len = codes.dim(candle::D::Minus1)?;
+                    let mut pcm_chunks = vec![];
+                    for chunk_start in (0..seq_len).step_by(chunk_size) {
+                        let chunk_len = usize::min(chunk_size, seq_len - chunk_start);
+                        let codes = codes.narrow(candle::D::Minus1, chunk_start, chunk_len)?;
+                        pcm_chunks.push(model.decode(&codes)?)
+                    }
+                    Tensor::cat(&pcm_chunks, candle::D::Minus1)?
+                }
+                None => model.decode(&codes)?,
+            };
             println!("output pcm shape: {:?}", pcm.shape());
             let pcm = pcm.i(0)?.i(0)?;
             let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;