AI Briefing#h100
The latest AI and developer news about #h100, with the original source and a short summary.
The latest AI and developer news about #h100, with the original source and a short summary.
On-demand H100 SXM 80GB rates range from $2.69 per hour on RunPod Community to $12.29 on Azure, a 4.57x difference for the same hardware.
Linum AI releases P-JiT, a 2B pixel-space text-to-image model trained for $21,038, which removes the VAE to achieve 4x attention context reduction.
Current H100 rental prices are $3.52/hour, but supply constraints for AGI agents could force a 10x increase.
A new interactive tool estimates GPU requirements for serving large language models like Llama 3.3 70B and DeepSeek V4 Pro, using benchmark data for H100 and B200 hardware.
The new Quail system processes over 1 billion tokens per minute on a single H100 GPU, achieving more than 10x the speed of the vLLM baseline for AI-SQL workloads.
Cohere released a Megakernel serving engine for the North Mini Code model, achieving up to 1.41x end-to-end inference speedup over vLLM on a single H100 in BF16.
As AI models become more intelligent, the value they generate from the same amount of compute will grow, driving a sharp rise in compute costs.
In the GPU rental market, the supply of multiple identical GPU clusters actually needed for real workloads is far more limited than single-GPU supply, and the pricing structure differs as well.
HRM-Text has released a method for pretraining a 1B model for about $1000.
A single H100 80GB benchmark confirmed the advantage of Gemma 4 E2B and Qwen 3.6 A3B FP8.
In a single H100 GPU benchmark, Gemma 4 E2B-it delivered the best results in both throughput and TTFT.
Unweight reduces LLM weights by 15~22% on H100 while keeping outputs bit-exact.
ARC post-training generates 12 seconds of 44.1kHz stereo audio in 75ms on an H100.