Deployment & engineering

PrismML released Binary and Ternary Bonsai Image 4B, 1-bit and ternary text-to-image diffusion transformers around 3GB, compared with FLUX.2 Klein 4B at about 16GB, with browser-local WebGPU demo links and an Apache-2.0 license disclosed in the Reddit snippet.

Qualcomm will supply chips to ByteDance for artificial intelligence data centers, according to people familiar with the matter; the post does not disclose chip models, order volume, pricing, or delivery timing.

dlmserve released an MIT-licensed serving engine for diffusion language models, with LLaDA-8B-Instruct support and 2.5x HF throughput at batch=4. It exposes an OpenAI-compatible /v1/chat/completions API, batches at the denoising-step level, runs in 12GB VRAM, and adds about 1.8x throughput with optional LocalLeap acceleration.

OpenRouter raised a $113 million Series B led by CapitalG; its weekly volume rose from 5 trillion to 25 trillion tokens over the past 6 months.

Nanjing University LAMDA and Alibaba Intelligent Engine proposed DAR, a timestep-aware cross-layer routing method that replaces fixed residual accumulation in DiT; on ImageNet 256x256, it reduced SiT-XL/2 FID from 9.67 to 7.56 and reached baseline convergence quality with 8.75x fewer training iterations.

ModelBest released ForgeTrain and MiniCPM5-1B, saying ForgeTrain was written by AI and trains 10% faster than NVIDIA Megatron under the same hardware conditions. MiniCPM5-1B is a 1B-parameter edge model with about 2GB FP16 weights and about 0.5GB INT4/Q4 weights.

ModelBest open-sourced MiniCPM5-1B and ForgeTrain; the 1B edge model scores 17.9 on AA-Index, while the AI-written ForgeTrain framework matches Megatron’s training results and runs 10% faster on Nvidia H100 under the article’s reported setup.

ModelBest open-sourced MiniCPM5-1B, a 1B-parameter edge language model that beats all sub-2B models on AA-Index, uses a 0.5GB weight file after INT4 quantization, and runs on phones and browsers.

Greg Brockman recounted OpenAI’s 72-hour crisis: on November 17, 2023, the board removed Sam Altman as CEO and took Brockman off the board, after which Brockman resigned the same day and said he initially put the chance of taking the company back at 10%.

Shard reduces Llama-3.1-8B KV memory by about 10× at 8K context and 11× at 32K, with no measured drop on NIAH or LongBench, using PCA plus int4 quantization for K and Hadamard rotation plus vector quantization for V.

Developers found an unannounced OpenAI GPT-5.6 entry in Codex backend logs under the codename iris-alpha, with a 1.5 million-token context window, about 43% higher than GPT-5.5’s 1.05 million-token limit.

Apple is reportedly using a custom 1.2T-parameter Google model to run parts of the next-generation Siri, while simpler queries are expected to run on-device; the post says response speed for everyday questions is the key constraint.

RTPurbo converts full-attention LLMs to sparse inference with a few hundred adaptation steps. It keeps the full KV cache only for retrieval heads, uses a 16-dimensional token indexer, and reports up to 9.36x prefill speedup at 1M context plus about 2.01x decode speedup on long-context and reasoning benchmarks.

Reasonix uses an append-only loop for DeepSeek V4 and reports a 99.82% cache hit rate in long coding sessions, cutting an example 400M-token bill from $61 to $12.

Epoch AI says memory has grown to nearly two-thirds of AI chip component costs; the RSS body only lists the article URL, 68 points, and 71 comments, and the post does not disclose the methodology or sample scope.

OpenBMB released BitCPM-CANN, a 1.58-bit QAT training stack on Ascend NPU with 0.5B, 1B, 3B, and 8B models trained from scratch, where the 1B to 8B variants retain 95.7%–97.2% of full-precision MiniCPM4 performance across 11 benchmarks.

Reddit user hopbel tested Qwen3.6 27B with approximate KLD on wikitext-2 at 16k context, using Q5_K_M as the proxy baseline; Q5_K_S weights with q4_0 KV cache scored 0.016304, while Q4_K_XL with f16 KV cache scored 0.026067, so weight quant tier dominated KV-cache quant in this setup.

Zhejiang University and the University of Adelaide introduced FlashAR, using 0.05% of the original training data to reduce Emu3.5-Image-34B 512×512 generation latency from 130.10 seconds to 5.68 seconds, while GenEval changed from 80.48 to 80.29.

Yoshua Bengio’s team introduced GRAM, a generative recursive reasoning model that samples multiple latent trajectories; on Sudoku-Extreme, GRAM reached 97.0% accuracy with 16 recursive steps and 20 parallel samples, exceeding TRM’s 90.5% result at 320 serial recursive steps.

Mistral AI said it has reached a definitive agreement to acquire Emmi AI, a physics AI pioneer, to strengthen its position as an AI transformation partner for industrial companies. Austria-based Emmi AI works on physics AI and large engineering models that speed up engineering workflows, replace multi-day computations with real-time simulation and build digital twins. Emmi's co-founders and more than 30 researchers and engineers will join Mistral's Science and Applied AI teams in May.