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NInfer adds day-0 support for the Qwen3.8-27B model, achieving around 200 tokens per second on a single RTX 5090 with speculative decoding, and includes engine improvements like concurrent requests and kernel optimizations.
AutoLab is a new benchmark evaluating 17 frontier models on 36 expert-curated long-horizon tasks (system optimization, model development, CUDA kernels, puzzles), finding that persistence—not initial attempt quality—is the dominant predictor of success. Claude-opus-4.6 led all categories, while most other models terminated prematurely or exhausted budgets with minimal progress.
Modal has announced that replicas of vLLM and SGLang servers now start up 3-10x faster, leveraging improvements in GPU health management and CUDA context checkpointing.
The author presents a custom, hackable ML compiler written in Python that lowers LLMs to optimized CUDA kernels through a multi-stage IR pipeline, achieving performance competitive with or superior to PyTorch on specific operations. The article details the compiler's optimization passes, lowering rules, and CLI usage for generating efficient fused GPU kernels.
A developer shares local inference benchmarks and systemd configurations for running the Qwen3.6-27B model on an NVIDIA RTX Pro 4500 Blackwell GPU using llama.cpp. The post requests optimization tips for throughput and explores potential use cases for larger models.