All tags
Topic: "inference-scaling"
not much happened today
o3 o4-mini gpt-5 sonnet-3.7 gemma-3 qwen-2.5-vl gemini-2.5-pro gemma-7b llama-3-1-405b openai deepseek anthropic google meta-ai-fair inference-scaling reward-modeling coding-models ocr model-preview rate-limiting model-pricing architectural-advantage benchmarking long-form-reasoning attention-mechanisms mixture-of-experts gpu-throughput sama akhaliq nearcyan fchollet reach_vb philschmid teortaxestex epochairesearch omarsar0
OpenAI announced that o3 and o4-mini models will be released soon, with GPT-5 expected in a few months, delayed for quality improvements and capacity planning. DeepSeek introduced Self-Principled Critique Tuning (SPCT) to enhance inference-time scalability for generalist reward models. Anthropic's Sonnet 3.7 remains a top coding model. Google's Gemma 3 is available on KerasHub, and Qwen 2.5 VL powers a new Apache 2.0 licensed OCR model. Gemini 2.5 Pro entered public preview with increased rate limits and pricing announced, becoming a preferred model for many tasks except image generation. Meta's architectural advantage and the FrontierMath benchmark challenge AI's long-form reasoning and worldview development. Research reveals LLMs focus attention on the first token as an "attention sink," preserving representation diversity, demonstrated in Gemma 7B and LLaMa 3.1 models. MegaScale-Infer offers efficient serving of large-scale Mixture-of-Experts models with up to 1.90x higher per-GPU throughput.
DeepSeek #1 on US App Store, Nvidia stock tanks -17%
deepseek-r1 deepseek-v3 qwen2.5-vl o1 deepseek openai nvidia langchain moe-architecture chain-of-thought fp8-precision multimodality vision agentic-ai inference-scaling gpu-optimization model-efficiency ai-chatbots memory-integration tool-use stock-market-reactions sama mervenoyann omarasar0 teortaxestex nptacek carpeetti finbarrtimbers cwolferesearch arthurrapier danhendrycks scaling01 janusflow
DeepSeek has made a significant cultural impact by hitting mainstream news unexpectedly in 2025. The DeepSeek-R1 model features a massive 671B parameter MoE architecture and demonstrates chain-of-thought (CoT) capabilities comparable to OpenAI's o1 at a lower cost. The DeepSeek V3 model trains a 236B parameter model 42% faster than its predecessor using fp8 precision. The Qwen2.5 multimodal models support images and videos with sizes ranging from 3B to 72B parameters, featuring strong vision and agentic capabilities. LangChain and LangGraph integration enable AI chatbots with memory and tool use, including applications like the DeFi Agent. Discussions highlight NVIDIA's role in hardware acceleration, with concerns about stock drops due to DeepSeek's efficiency and market fears. The compute demand is expected to rise despite efficiency gains, driven by inference scaling and MoE design improvements.