bf16
5 stories and discussions about bf16, aggregated from every source we track.
Google's QAT Gemma 4 E2B keeps its embedding tables in bf16, and on a Tesla T4 they are most of the model. Packing them to int4 on the grid QAT trained them onto cuts model loading from 6.33 to 2.86 GiB, with every greedy test output token-identical, and raises vLLM's output throughput 11-37% over Google's own W4A16 export.
A short background on SageMaker real-time endpoints, then a measured comparison of Gemma 4 E2B's QAT w4a16 checkpoint against the full-size bf16 release on the same NVIDIA L4 endpoint: decode speed, parallel throughput, answers and cost.
A step by step deployment of Gemma 4 E2B with vLLM on a single Tesla T4 attached to a Compute Engine VM, and a measured comparison of the QAT w4a16 checkpoint against the bf16 reference on the same card.
VeriLoop E2 is now publicly available: a 27B post-trained model built on Qwen3.8-27B for verifiable code, mathematics, scientific reasoning, and long-horizon agentic problem solving. At the center of E2 is…
A short background on SageMaker real-time endpoints, then a measured comparison of Gemma 4 E2B's QAT w4a16 checkpoint against the full-size bf16 release on the same NVIDIA L4 endpoint: decode speed, parallel throughput, answers and cost.