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Nvidia's Tesla?? Ever heard of it?

Nvidia, the ruling graphics card producer in the market to this current time, with its leading graphics card being RTX 3090, which is very powerful, but is something even powerful than one RTX 3090, well you can say two RTX 3090's but like literally is something more powerful than the 3090. You might say Titan RTX, but Nah 3090 beats titan by a big margin in a performance boost.

Well yeah, 3090 rules the market while not being in the market, but is the RTX lineup best? Well, we also have the RDNA2 Graphics card by AMD, a pretty hard competitor, other than that Nvidia also has a GTX lineup which is outdated by now, but is there another strong lineup of Graphics card?

Nvidia has another lineup of Graphics cards, called Tesla, and another lineup called Quadro, but let's just look at the Tesla lineup for now.

SO WHAT IS NVIDIA TESLA?

Tesla GPUs are concentrated on high-performance computing than gaming and renders, these are purely made for computations, Deep AI learning, made for workstations, and these are beasts of a graphics cards.

The leading Graphics card in the Tesla lineup is A100. Tesla A100 was marked at 4 times better performance than its previous Tesla graphics card V100. A100 is a hefty beast, like literally, this Graphics card is made on the Ampere architecture, successor to the Volta architecture, which is aimed to be used in AI learning and other heavy computational tasks.

SPECS-

  • GPU - GA100
  • PCIe 4.0 x16
  • CUDA cores - 6912
  • Tensor cores - 432
  • 40 GB HBM2e Memory
  • Memory Bandwidth - 1.6 TB/s 
  • Base Clock Speed - 1,095 MHz
  • Boosted Clock Speed - 1,410 MHz
  • TDP - 400W
  • Outputs - None
  • Transistors - 54.2 billion
  • Process Size - 7 nm
  • TFLOPS - 312 in deep learning 
  • L2 Cache - 40 MB

With its 40 GB HBM2e memory grants it a memory bandwidth of 1.6 TB/s, compared to 19 GB/s of 3090, yeah not a good comparison, well but A100's memory bandwidth is 1.7 times greater than V100, which had a bandwidth of 900 GB/s.
With all this power, it can accelerate the data center's performance by a lot and can the performance in AI, data analytics is highly increased, and with its Multi-instance technology, it can be partitioned into 7 isolated GPUs instances to improve upon the workloads. 
And its 40 MB L2 Cache which is 70% more than the V100, for maximizing the computational power, and a 2.3 times increase of bandwidth than V100. And the L2 cache is shared amongst GPCs, SMs which improves the performance for HPC and AI workloads, and bigger data sheets and models can be stored on Cache. 


DGX-A100 is a server system for workloads, it features eight A100s, and up to 5 PetaFLOPS, giving data scientists the ability to test AI to much higher extents.
A universal system for AI Data Centers, scaling up AI training and scientific computing.

CAN IT GAME?
Well in simple words, like yeah you can game with Tesla GPUs, but why would you do that? Because these are centered around Data centers and pure computational power, used in supercomputers. But yeah you can game won't, but there are no merits to it, it will probably give less performance than RTX, so yeah don't buy these for gaming, but if you wanna start up a deep AI learning system for an enterprise, yeah probably.

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