Question: I just got my new MacBook Pro with M1 Max chip and am setting up Python. I've tried several combinational settings to test speed - now I'm quite confused. First put my questions here:  Why python run natively on M1 Max is greatly (~100%) slower than on my old MacBook Pro 2016 with Intel i5? On M1 Max, why there isn't significant speed difference between native run (by miniforge) and run via Rosetta (by anaconda) - which is supposed to be slower ~20%? On M1 Max and native run, why there isn't significant speed difference between conda installed Numpy and TensorFlow installed Numpy - which is supposed to be faster? On M1 Max, why run in PyCharm IDE is constantly slower ~20% than run from terminal, which doesn't happen on my old Intel Mac.  Evidence supporting my questions is as follows:  Here are the settings I've tried: 1. Python installed by  Miniforge-arm64, so that python is natively run on M1 Max Chip. (Check from Activity Monitor, Kind of python process is Apple). Anaconda. Then python is run via Rosseta. (Check from Activity Monitor, Kind of python process is Intel).  2. Numpy installed by  conda install numpy: numpy from original conda-forge channel, or pre-installed with anaconda. Apple-TensorFlow: with python installed by miniforge, I directly install tensorflow, and numpy will also be installed. It's said that, numpy installed in this way is optimized for Apple M1 and will be faster. Here is the installation commands:  conda install -c apple tensorflow-deps python -m pip install tensorflow-macos python -m pip install tensorflow-metal  3. Run from  Terminal. PyCharm (Apple Silicon version).   Here is the test code: import time import numpy as np np.random.seed(42) a = np.random.uniform(size=(300, 300)) runtimes = 10 timecosts = [] for _ in range(runtimes): s_time = time.time() for i in range(100): a += 1 np.linalg.svd(a) timecosts.append(time.time() - s_time) print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s')  and here are the results: +-----------------------------------+-----------------------+--------------------+ | Python installed by (run on)→ | Miniforge (native M1) | Anaconda (Rosseta) | +----------------------+------------+------------+----------+----------+---------+ | Numpy installed by ↓ | Run from → | Terminal | PyCharm | Terminal | PyCharm | +----------------------+------------+------------+----------+----------+---------+ | Apple Tensorflow | 4.19151 | 4.86248 | / | / | +-----------------------------------+------------+----------+----------+---------+ | conda install numpy | 4.29386 | 4.98370 | 4.10029 | 4.99271 | +-----------------------------------+------------+----------+----------+---------+  This is quite slow. For comparison,  run the same code on my old MacBook Pro 2016 with i5 chip - it costs 2.39917s. another post (but not in English) reports that run with M1 chip (not Pro or Max), miniforge+conda_installed_numpy is 2.53214s, and miniforge+apple_tensorflow_numpy is 1.00613s. you may also try on it your own.  Here is the CPU information details:  My old i5:  $ sysctl -a | grep -e brand_string -e cpu.core_count machdep.cpu.brand_string: Intel(R) Core(TM) i5-6360U CPU @ 2.00GHz machdep.cpu.core_count: 2   My new M1 Max:  % sysctl -a | grep -e brand_string -e cpu.core_count machdep.cpu.brand_string: Apple M1 Max machdep.cpu.core_count: 10   I follow instructions strictly from tutorials - but why would all these happen? Is it because of my installation flaws, or because of M1 Max chip? Since my work relies heavily on local runs, local speed is very important to me. Any suggestions to possible solution, or any data points on your own device would be greatly appreciated :)

Answer: Update Mar 28 2022: Please see @AndrejHribernik's comment below.  How to install numpy on M1 Max, with the most accelerated performance (Apple's vecLib)? Here's the answer as of Dec 6 2021.  Steps I. Install miniforge So that your Python is run natively on arm64, not translated via Rosseta.  Download Miniforge3-MacOSX-arm64.sh, then Run the script, then open another shell  $ bash Miniforge3-MacOSX-arm64.sh   Create an environment (here I use name np_veclib)  $ conda create -n np_veclib python=3.9 $ conda activate np_veclib  II. Install Numpy with BLAS interface specified as vecLib  To compile numpy, first need to install cython and pybind11:  $ conda install cython pybind11   Compile numpy by (Thanks @Marijn's answer) - don't use conda install!  $ pip install --no-binary :all: --no-use-pep517 numpy   An alternative of 2. is to build from source  $ git clone https://github.com/numpy/numpy $ cd numpy $ cp site.cfg.example site.cfg $ nano site.cfg  Edit the copied site.cfg: add the following lines: [accelerate] libraries = Accelerate, vecLib  Then build and install: $ NPY_LAPACK_ORDER=accelerate python setup.py build $ python setup.py install   After either 2 or 3, now test whether numpy is using vecLib:  >>> import numpy >>> numpy.show_config()  Then, info like /System/Library/Frameworks/vecLib.framework/Headers should be printed. III. For further installing other packages using conda Make conda recognize packages installed by pip conda config --set pip_interop_enabled true  This must be done, otherwise if e.g. conda install pandas, then numpy will be in The following packages will be installed list and installed again. But the new installed one is from conda-forge channel and is slow.  Comparisons to other installations: 1. Competitors: Except for the above optimal one, I also tried several other installations  A. np_default: conda create -n np_default python=3.9 numpy B. np_openblas: conda create -n np_openblas python=3.9 numpy blas=*=*openblas* C. np_netlib: conda create -n np_netlib python=3.9 numpy blas=*=*netlib*  The above ABC options are directly installed from conda-forge channel. numpy.show_config() will show identical results. To see the difference, examine by conda list - e.g. openblas packages are installed in B. Note that mkl or blis is not supported on arm64.  D. np_openblas_source: First install openblas by brew install openblas. Then add [openblas] path /opt/homebrew/opt/openblas to site.cfg and build Numpy from source. M1 and i9–9880H in this post. My old i5-6360U 2cores on MacBook Pro 2016 13in.  2. Benchmarks: Here I use two benchmarks:  mysvd.py: My SVD decomposition  import time import numpy as np np.random.seed(42) a = np.random.uniform(size=(300, 300)) runtimes = 10 timecosts = [] for _ in range(runtimes): s_time = time.time() for i in range(100): a += 1 np.linalg.svd(a) timecosts.append(time.time() - s_time) print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s')   dario.py: A benchmark script by Dario Radečić at the post above.  3. Results: +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | sec | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H | i5-6360U | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | mysvd | 1.02300 | 4.29386 | 4.13854 | 4.75812 | 12.57879 | / | / | 2.39917 | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+ | dario | 21 | 41 | 39 | 323 | 40 | 33 | 23 | 78 | +-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+