AI Briefing#inference
The latest AI and developer news about #inference, with the original source and a short summary.
The latest AI and developer news about #inference, with the original source and a short summary.
Qwen3.6 adds preserve_thinking, improving reasoning context and KV cache efficiency.
A benchmark running MiniMax M2.7 AWQ on 8x MI50 32GB hit a peak of 64 tok/s.
Cloudflare has unified its multi-model AI gateway with BYOM support.
A diffusion LLM has achieved both AR-level quality and parallel generation speed together.
The real performance of AI infrastructure is determined not by FLOPS but by cost per token.
Complaints about Ollama Cloud's response latency and low TPS have escalated to the point of subscription cancellations.
A 32B medical LLM built with SeedRL was released, with thinking mode set as the default.
With DFlash in oMLX 0.3.5 RC1, Qwen3.5 27B went from 9 to 22 T/S on a Mac M5 Max.
Splitting core/scratch compresses reasoning KV cache smaller and more accurately.
A method that converts auto-regressive models into diffusion models to make them **more than 2x** faster.
The nondeterminism of LLM inference is less about GPU concurrency and more about request scheduling.
KIV aims for a 1M token context window on an RTX 4070 12GB without retraining.
A low-cost OCR stack along with free credits and research support sources was shared for processing 64M pages.
The terminal is fast, but a local LLM trap emerged where hooking it up to an app took as long as 13 minutes.
Warp Decode shifts the parallelization axis of MoE inference from experts to outputs on Blackwell GPUs, boosting performance by 1.84x.
In response to Anthropic's restrictions on Claude access, this explains how to switch OpenClaw agents to open models.
Telecom operators are partnering with NVIDIA to transform existing network infrastructure into **AI grids** for distributed AI inference.
GGML, the team behind llama.cpp, has joined Hugging Face to strengthen support for the local AI ecosystem.
OpenAI has signed a partnership with Cerebras to bring in 750MW of ultra low-latency AI computing resources, revolutionizing AI inference speed.
This piece explains in detail, from the fundamentals, how continuous batching works to maximize LLM inference efficiency.
SGLang now supports the Transformers backend, enabling high-performance inference for any Transformers-compatible model.
Groq's LPU-based ultra-fast inference service is now directly available on the Hugging Face Hub.
Furiosa has released SDK 2025.1.0, which optimizes LLM inference performance and supports Tool Calling.
As the AI industry shifts from training to inference, the importance of efficient inference computing is growing.