How do you retain reinforcement studying for massive reasoning fashions from stalling on a couple of very lengthy, very sluggish rollouts whereas GPUs sit beneath used? a crew of researchers from Moonshot AI and Tsinghua University introduce ‘Seer’, a brand new on-line context studying system that targets a selected programs bottleneck in reinforcement studying for…
Large language fashions at the moment are restricted much less by coaching and extra by how briskly and cheaply we will serve tokens underneath actual site visitors. That comes down to a few implementation particulars: how the runtime batches requests, the way it overlaps prefill and decode, and the way it shops and reuses the…
Large language mannequin serving usually wastes GPU reminiscence as a result of engines pre-reserve massive static KV cache areas per mannequin, even when requests are bursty or idle. Meet ‘kvcached‘, a library to allow virtualized, elastic KV cache for LLM serving on shared GPUs. kvcached has been developed by a analysis from Berkeley’s Sky Computing…
IBM simply launched Granite 4.0, an open-source LLM household that swaps monolithic Transformers for a hybrid Mamba-2/Transformer stack to chop serving reminiscence whereas maintaining high quality. Sizes span a 3B dense “Micro,” a 3B hybrid “H-Micro,” a 7B hybrid MoE “H-Tiny” (~1B lively), and a 32B hybrid MoE “H-Small” (~9B lively). The fashions are Apache-2.0,…
What MLPerf Inference Actually Measures? MLPerf Inference quantifies how briskly a whole system ({hardware} + runtime + serving stack) executes fastened, pre-trained fashions underneath strict latency and accuracy constraints. Results are reported for the Datacenter and Edge suites with standardized request patterns (“eventualities”) generated by LoadGen, guaranteeing architectural neutrality and reproducibility. The Closed division fixes…
IBM researchers, along with ETH Zürich, have unveiled a brand new class of Analog Foundation Models (AFMs) designed to bridge the hole between giant language fashions (LLMs) and Analog In-Memory Computing (AIMC) {hardware}. AIMC has lengthy promised a radical leap in effectivity—operating fashions with a billion parameters in a footprint sufficiently small for embedded or…
Table of contents Hardware Generation without Templates Input IR: Affine, Relation-Centric Semantics (Deconstruct) Front End: FU Graph + Memory Co-Design (Architect) Back End: Compile & Optimize to RTL (Compile & Optimize) Outcome Importance for each segment How the “Compiler for AI Chips” Works—Step-by-Step ? Where It Lands in the Ecosystem? Summary MIT researchers (Han Lab)…
MoonshotAI has open-sourced checkpoint-engine, a light-weight middleware aimed toward fixing one of many key bottlenecks in giant language mannequin (LLM) deployment: quickly updating mannequin weights throughout hundreds of GPUs with out disrupting inference. The library is especially designed for reinforcement studying (RL) and reinforcement studying with human suggestions (RLHF), the place fashions are up to…
Table of contents What actually determines performance on modern GPUs CUDA: nvcc/ptxas, cuDNN, CUTLASS, and CUDA Graphs ROCm: HIP/Clang toolchain, rocBLAS/MIOpen, and the 6.x series Triton: a DSL and compiler for custom kernels TensorRT (and TensorRT-LLM): builder-time graph optimization for inference Practical guidance: choosing and tuning the stack Deep-learning throughput hinges on how successfully a…
Why Do Sequential LLMs Hit a Bottleneck? Test-time compute scaling in LLMs has historically relied on extending single reasoning paths. While this strategy improves reasoning for a restricted vary, efficiency plateaus rapidly. Experiments on DeepSeek-R1-distill-Qwen-1.5B present that growing token budgets past 32K (up to 128K) yields negligible accuracy positive aspects. The bottleneck arises from early…