Deep dives into AI infrastructure, GPU performance benchmarks, customer stories, and the latest from the NeuralVane platform.
A deep dive into the network architecture behind NeuralVane's multi-region GPU clusters. We cover topology design, congestion control, and how we achieve near-linear scaling for distributed training jobs.
We ran identical LLM training workloads across three GPU generations on our platform. The results reveal surprising insights about memory bandwidth, interconnect utilization, and cost-per-token economics.
Meridian AI was spending $2.4M/month on GPU compute with a major cloud provider. After migrating to NeuralVane, they cut costs by 60% while improving training throughput by 3.2x. Here's their story.
Today we're launching NeuralVane Inference Engine — a fully managed serving platform optimized for LLMs and diffusion models. Automatic batching, speculative decoding, and global edge deployment built in.
GPUs fail. Nodes go down. Networks partition. In this post, we explain how NeuralVane's checkpoint-and-resume architecture ensures your training jobs survive hardware failures without losing progress.
Training large models requires feeding data at extraordinary rates. We benchmarked our distributed storage layer against S3, GCS, and local NVMe to show how NeuralVane eliminates I/O bottlenecks.
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