@wangkun025 brew install --cask squirrel

@joyhub2140 单核打单核可以 多核打不过

@shutongxinq 如果能给我 root && 能让我远程 && 不用跟别人抢 确实爽

@ly90907
@longbye0
egpu 确实是一个好方法 可是目前 m1 不支持 egpu 不过确实可以期待一下以后会不会支持

@longbye0 隔壁宿舍有一个用台式机的 天天跳闸😂

@mythabc 还一个问题是桌面级 3080 3090 不好买到吧

3080 mobile 对比 2080 ti

cuda cores 6,144 > 4,352

memory 16GB of GDDR6 > 11GB of GDDR6

@murmur 其实我对 3070 也没什么概念 不知道 3080mobile 能不能打 2080ti

@kiddingU 好的 学习了

用 sync.Map

原先用 mbp 的时候就拿 ipad pro+magic keyboard 远程 ssh 过去。可是这件事在我换了 m1 mba 之后就十分尴尬了。因为 mba 更轻😂

补充一个树莓派的😂

Dotted two 4096x4096 matrices in 10.18 s.
Dotted two vectors of length 524288 in 2.27 ms.
SVD of a 2048x1024 matrix in 6.67 s.
Cholesky decomposition of a 2048x2048 matrix in 0.85 s.
Eigendecomposition of a 2048x2048 matrix in 37.83 s.

This was obtained using the following Numpy configuration:
blas_info:
libraries = ['cblas', 'blas', 'cblas', 'blas']
library_dirs = ['/root/mambaforge/envs/maths/lib']
include_dirs = ['/root/mambaforge/envs/maths/include']
language = c
define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
define_macros = [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)]
libraries = ['cblas', 'blas', 'cblas', 'blas']
library_dirs = ['/root/mambaforge/envs/maths/lib']
include_dirs = ['/root/mambaforge/envs/maths/include']
language = c
lapack_info:
libraries = ['lapack', 'blas', 'lapack', 'blas']
library_dirs = ['/root/mambaforge/envs/maths/lib']
language = f77
lapack_opt_info:
libraries = ['lapack', 'blas', 'lapack', 'blas', 'cblas', 'blas', 'cblas', 'blas']
library_dirs = ['/root/mambaforge/envs/maths/lib']
language = c
define_macros = [('NO_ATLAS_INFO', 1), ('HAVE_CBLAS', None)]
include_dirs = ['/root/mambaforge/envs/maths/include']

@Jim142857 MAS 版本==365 版本

logo 都是一样的。。。也都是 arm 原生

@chonger 点官网链接旁边的小三角可以选 apple silicon

https://i.imgur.com/SACEgJ8.png

@paradoxs 大量的开源软件不能让作者每年交 100 刀给 Apple 吧

@paradoxs 等 M$一句话 apple silicon 设备立马有 bootcamp