diff --git a/candle-core/examples/cpu_benchmarks.rs b/candle-core/examples/cpu_benchmarks.rs
new file mode 100644
index 00000000..f9b28a4e
--- /dev/null
+++ b/candle-core/examples/cpu_benchmarks.rs
@@ -0,0 +1,81 @@
+/// This example contains some simple benchmarks so that it's easy to run them in perf etc.
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+// use candle::quantized::GgmlType;
+use candle::{DType, Device, Result, Tensor};
+// use clap::{Parser, Subcommand};
+
+// fn softmax<D: candle::shape::Dim>(xs: &Tensor, dim: D) -> Result<Tensor> {
+//     let dim = dim.to_index(xs.shape(), "softmax")?;
+//     let max = xs.max_keepdim(dim)?;
+//     let diff = xs.broadcast_sub(&max)?;
+//     let num = diff.exp()?;
+//     let den = num.sum_keepdim(dim)?;
+//     num.broadcast_div(&den)
+// }
+
+trait Benchmark {
+    type PreProcessData;
+    type RunResult;
+
+    fn preprocess() -> Result<Self::PreProcessData>;
+    fn run_one(_: &Self::PreProcessData) -> Result<Self::RunResult>;
+
+    const ITERS: usize;
+}
+
+struct Matmul;
+impl Benchmark for Matmul {
+    type PreProcessData = (Tensor, Tensor);
+    type RunResult = Tensor;
+    fn preprocess() -> Result<Self::PreProcessData> {
+        let lhs = Tensor::randn((1024, 1024), DType::F32, &Device::Cpu, 1.0, 0.0)?;
+        let rhs = Tensor::randn((1024, 1024), DType::F32, &Device::Cpu, 1.0, 0.0)?;
+        Ok((lhs, rhs))
+    }
+
+    fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
+        d.0.matmul(&d.1)
+    }
+
+    const ITERS: usize = 100;
+}
+
+// struct Softmax;
+// impl Benchmark for Softmax {
+//     type PreProcessData = Tensor;
+//     type RunResult = Tensor;
+//     fn preprocess() -> Result<Self::PreProcessData> {
+//         // Typical whisper tiny size.
+//         let x = Tensor::randn(0f32, 1., (1, 6, 200, 1500), &Device::Cpu)?;
+//         Ok(x)
+//     }
+//
+//     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
+//         softmax(d, D::Minus1)
+//     }
+//
+//     const ITERS: usize = 100;
+// }
+
+fn run<B: Benchmark>(iters: Option<usize>) -> Result<()> {
+    use std::hint::black_box;
+
+    let iters = iters.unwrap_or(B::ITERS);
+    let d = B::preprocess()?;
+    let start = std::time::Instant::now();
+    for _iter in 0..iters {
+        let _res = black_box(B::run_one(black_box(&d))?);
+    }
+    println!("{:?}", start.elapsed() / iters as u32);
+    Ok(())
+}
+
+fn main() -> Result<()> {
+    run::<Matmul>(None)?;
+    Ok(())
+}