BLAS stands for Basic Linear Algebra Subprograms. BLAS level is a measure of the optimization of a library for performing basic linear algebra operations. The higher the BLAS level, the more optimized the library is. The BLAS level is important for determining the performance of a library on different architectures. 

BLAS level 1 is the most basic level, and is typically not optimized for any specific architecture. BLAS level 2 is slightly more optimized, and is typically tuned for a specific CPU architecture. BLAS level 3 is highly optimized, and is typically tuned for multiple architectures. The highest possible BLAS level is 4, which is only achievable by hand-optimizing code for a specific architecture. 

At each level, the library typically supports a larger set of algorithms and functions, as well as improved performance. Furthermore, libraries at higher BLAS levels tend to be more robust and easier to maintain. As such, choosing a library with an appropriate BLAS level can drastically improve the performance of your application.  

It is important to note that optimized linear algebra operations may require additional dependencies (such as compiler flags or other libraries). It is therefore recommended that you test any library before using it in production. Additionally, for many scientific applications, selecting a library with a high BLAS level can have significant benefits in terms of both speed and accuracy. 

The CS-2 also features high performance implementation of BLAS to facilitate efficient matrix computations and accelerate the execution of deep learning applications. This makes it an ideal choice for AI developers who are looking for architectures that can deliver superior results while keeping cost and power consumption low.  

By leveraging the performance benefits of BLAS, Cerebras Systems has pushed the boundaries of what is possible with artificial intelligence technology. With its innovative chip design, they have been able to create a powerful processor that is capable of delivering superior results compared to traditional processors while maintaining efficiency and low power consumption. This breakthrough in computer architecture and BLAS optimization demonstrates how Cerebras Systems is revolutionizing the world of artificial intelligence. 

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