Published on: 2025-06-05 06:31:07
Why is DeepSeek-V3 supposedly fast and cheap to serve at scale, but too slow and expensive to run locally? Why are some AI models slow to respond but fast once they get going? AI inference providers often talk about a fundamental tradeoff between throughput and latency: for any given model, you can either serve it at high-throughput high-latency, or low-throughput low-latency. In fact, some models are so naturally GPU-inefficient that in practice they must be served at high-latency to have any
Keywords: batch inference model token tokens
Find related items on AmazonPublished on: 2025-06-04 22:49:22
Cerebras Breaks the 2,500 Tokens Per Second Barrier with Llama 4 Maverick 400B SUNNYVALE CA – May 28, 2025 -- Last week, Nvidia announced that 8 Blackwell GPUs in a DGX B200 could demonstrate 1,000 tokens per second (TPS) per user on Meta’s Llama 4 Maverick. Today, the same independent benchmark firm Artificial Analysis measured Cerebras at more than 2,500 TPS/user, more than doubling the performance of Nvidia’s flagship solution. “Cerebras has beaten the Llama 4 Maverick inference speed recor
Keywords: cerebras inference llama nvidia tokens
Find related items on AmazonPublished on: 2025-06-09 09:00:00
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Snowflake has thousands of enterprise customers that use the company’s data and AI technologies. Though many issues with generative AI are solved there is still lots of room for improvement. Two such issues are text-to-SQL query and AI inference. SQL is the query language used for databases and it has been around in various forms for over 50 years. Existing large langu
Keywords: ai arctic inference sql text
Find related items on AmazonPublished on: 2025-06-25 03:37:47
llm-d is a Kubernetes-native high-performance distributed LLM inference framework - a well-lit path for anyone to serve at scale, with the fastest time-to-value and competitive performance per dollar for most models across most hardware accelerators. With llm-d, users can operationalize gen AI deployments with a modular, high-performance, end-to-end serving solution that leverages the latest distributed inference optimizations like KV-cache aware routing and disaggregated serving, co-designed
Keywords: inference latency llm performance vllm
Find related items on AmazonPublished on: 2025-07-06 20:16:02
FastVLM: Efficient Vision Encoding for Vision Language Models This is the official repository of FastVLM: Efficient Vision Encoding for Vision Language Models. (CVPR 2025) Highlights We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images. Our smallest variant outperforms LLaVA-OneVision-0.5B with 85x faster Time-to-First-Token (TTFT) and 3.4x smaller vision encoder. Our larger variants using Qwe
Keywords: apple fastvlm inference models vision
Find related items on AmazonPublished on: 2025-07-07 06:16:02
FastVLM: Efficient Vision Encoding for Vision Language Models This is the official repository of FastVLM: Efficient Vision Encoding for Vision Language Models. (CVPR 2025) Highlights We introduce FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images. Our smallest variant outperforms LLaVA-OneVision-0.5B with 85x faster Time-to-First-Token (TTFT) and 3.4x smaller vision encoder. Our larger variants using Qwe
Keywords: apple fastvlm inference models vision
Find related items on AmazonPublished on: 2025-07-09 09:46:57
INTELLECT-2 Release: The First 32B Parameter Model Trained Through Globally Distributed Reinforcement Learning We're excited to release INTELLECT-2, the first 32B parameter model trained via globally distributed reinforcement learning. Unlike traditional centralized training efforts, INTELLECT-2 trains a reasoning language model using fully asynchronous RL across a dynamic, heterogeneous swarm of permissionless compute contributors. To enable a training run with this unique infrastructure, we
Keywords: decentralized inference model rl training
Find related items on AmazonPublished on: 2025-07-26 12:35:00
Train GRPO-powered RL agents with minimal code changes and maximal performance! Agent Reinforcement Trainer (ART) ART is an open-source reinforcement training library for improving LLM performance in agentic workflows. ART utilizes the powerful GRPO reinforcement learning algorithm to train models from their own experiences. Unlike most RL libraries, ART allows you to execute agent runs in your existing codebase while offloading all the complexity of the RL training loop to the ART backend. Re
Keywords: art inference loop server training
Find related items on AmazonPublished on: 2025-08-11 03:42:52
Local LLM inference Amir Zohrenejad · Follow 4 min read · Just now Just now -- Listen Share Tremendous progress, but not ready for production I stumbled into local inference during a side quest. The main mission was to build a text-to-SQL product. Curiosity hijacked the roadmap: could I cram an entire GenBI stack into the browser? The prototype never shipped. But I did fall into the local inference rabbit hole. Though “AI inference” is not a listed feature on my laptop spec sheet — through th
Keywords: built compute edge inference local
Find related items on AmazonPublished on: 2025-08-28 06:05:30
Open software capabilities for training and inference The real value of hardware is unlocked by co-designed software. AI Hypercomputer’s software layer helps AI practitioners and engineers move faster with open and popular ML frameworks and libraries such as PyTorch, JAX, vLLM, and Keras. For infrastructure teams, that translates to faster delivery times and more cost-efficient resource utilization. We’ve made significant advances in software for both AI training and inference. Pathways on Clo
Keywords: ai cluster gke inference training
Find related items on AmazonPublished on: 2025-08-30 05:00:23
Joan Cros/NurPhoto via Getty Images Everyone and their dog is investing in AI, but Google has more reason than most to put serious effort into its offerings. As Google CEO Sundar Pichai said in an internal meeting before last year's holidays: "In 2025, we need to be relentlessly focused on unlocking the benefits of [AI] technology and solve real user problems." Also: The most popular AI tools of 2025 (and what that even means) To help realize that vision, at the Google Cloud Next 2025 event i
Keywords: ai cluster gke google inference
Find related items on AmazonPublished on: 2025-08-30 05:05:00
Google During its Google Cloud Next 25 event Wednesday, the search giant unveiled the latest version of its Tensor Processing Unit (TPU), the custom chip built to run artificial intelligence -- with a twist. Also: Why Google Code Assist may finally be the programming power tool you need For the first time, Google is positioning the chip for inference, the making of predictions for live requests from millions or even billions of users, as opposed to training, the development of neural networks
Keywords: ai chip google inference ironwood
Find related items on AmazonPublished on: 2025-09-03 20:15:00
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The AI landscape continues to evolve at a rapid pace, with recent developments challenging established paradigms. Early in 2025, Chinese AI lab DeepSeek unveiled a new model that sent shockwaves through the AI industry and resulted in a 17% drop in Nvidia’s stock, along with other stocks related to AI data center demand. This market reaction was widely reported to stem
Keywords: ai deepseek inference models training
Find related items on AmazonPublished on: 2025-10-02 08:03:54
Have researchers discovered a new AI “scaling law”? That’s what some buzz on social media suggests — but experts are skeptical. AI scaling laws, a bit of an informal concept, describe how the performance of AI models improves as the size of the datasets and computing resources used to train them increases. Until roughly a year ago, scaling up “pre-training” — training ever-larger models on ever-larger datasets — was the dominant law by far, at least in the sense that most frontier AI labs embra
Keywords: ai inference model scaling time
Find related items on AmazonPublished on: 2025-10-03 19:44:14
NVIDIA Dynamo | Guides | Architecture and Features | APIs | SDK | NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as: Disaggregated prefill & decode inference – Maximizes GPU throughput and facilitates trade off between throughput and latency.
Keywords: dynamo inference llm run throughput
Find related items on AmazonPublished on: 2025-10-16 16:30:00
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cerebras Systems, an AI hardware startup that has been steadily challenging Nvidia’s dominance in the artificial intelligence market, announced Tuesday a significant expansion of its data center footprint and two major enterprise partnerships that position the company to become the leading provider of high-speed AI inference services. The company will add six new AI da
Keywords: ai cerebras inference models speed
Find related items on AmazonPublished on: 2025-11-04 21:00:00
OpenInfer has raised $8 million in funding to redefine AI inference for edge applications. It’s the brain child of Behnam Bastani and Reza Nourai, who spent nearly a decade of building and scaling AI systems together at Meta’s Reality Labs and Roblox. Through their work at the forefront of AI and system design, Bastani and Nourai witnessed firsthand how deep system architecture enables continuous, large-scale AI inference. However, today’s AI inference remains locked behind cloud APIs and host
Keywords: ai devices inference models openinfer
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