Best NVIDIA GPUs for Serving Inference on CoreWeave
When businesses use generalized cloud infrastructure, they are compelled to choose between two alternatives which are not preferred. They could either choose a general purpose GPU (Graphics Processing Unit), which is low on power but it is certainly affordable. Or they could choose something that has full-on performance but could cost them a significant amount.
So, you need to weigh the factors that help assess what kind of GPU would best suit your business. The GPUs should be able to handle the computation and be scalable at the given cost.
There are a variety of GPUs available in the market that you can choose from. But, allow us to introduce CoreWeave. It is an inference service that does not throw you into the jeopardy of choosing price or performance. Instead, you get both, ensuring your peace of mind.
What Are The Advantages Of Using CoreWeave Inference Service?
Using CoreWeave gives many benefits to the companies; they are as follows:
- You get to choose the services of a cloud provider who specializes in providing high-performance GPUs and uses state-of-the-art technology.
- You can assign complex AI and ML workloads to these GPUs and get quicker results.
- These GPUs are high on performance and cost-effective. They can reduce idle computing time and serve the end-user demand in real time, so it is extremely fast. It also has a lower inference latency.
- You can also choose from a wide range of GPUs per your requirements.
- You can scale the systems as per your requirement, so you do not need to make hardware and software changes as your workload increases and your business grows.
Given below are some GPU models that are serving in our arsenal:
NVIDIA A100 80GB
- It has twice the RAM and has 30% extra memory bandwidth when compared with the A100 40GB PCI-E,
- It is the best single GPU for large model inference.
- The 20B models run fast on an A100 80GB PCI-E. It is as efficient as the 13B models on an A6000 system.
- It is highly ideal for distributed training and inference. This is useful when model parallelism is required. An A100 NVLINK is recommended in this case.
NVIDIA A100 40GB
- If you want a high performer that has almost double the performance of the A40 or the A6000, then the A100 40GB PCI-E is the right GPU
- It nearly doubles the performance on a single GPU and can handle many workloads because of the 2x memory bandwidth.
- But it has 8GB less RAM as compared to the A40, and that is why you cannot host the larger models like the GPT NeoX 20B on a single GPU.
- To solve this issue, you can use the pairs of A100 PCI-E to make the best inference nodes.
NVIDIA RTX 5000
- It can run model inference for the GPT-J 6B or Fairseq 6.7B.
- It has twice the RAM, which means a bit more memory bandwidth than the RTX 4000
- This GPU has a quicker base clock rate as compared to the RTX4000.
- It runs 2.7B models RAM with a larger context.
NVIDIA A40
- For large-scale training, the A40 is CoreWeave’s recommended GPU.
- The A6000 is nimbler hence the faster GPU, but the A40 is a more robust GPU driver and has more availability of cores.
- The A40 has around 48GB of RAM, and you can use it for batch-training and fine-tuning Machine Learning Models.
To wrap up, these are some of the notable GPUs you can use virtually to run various business operations. Moreover, with E2E Networks, you can skip investing heavily in infrastructure and use a more accessible cloud network system to scale your business quickly.
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