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A collection of 74 posts

Nemotron CLIMB Is NVIDIA Admitting the Expensive Part of Scaling Laws Needs Cheaper Test Fixtures

Nemotron CLIMB Is NVIDIA Admitting the Expensive Part of Scaling Laws Needs Cheaper Test Fixtures

The least glamorous way to save millions of dollars in AI is to find out your training recipe is bad before you run it at the size where the invoice becomes memorable. That is the real story behind NVIDIA’s Nemotron-CLIMB proxy models: two small base models, 62M and 350M

NVIDIA’s New Streaming ASR Model Is the Boring Multilingual Agent Component Everyone Eventually Needs

NVIDIA’s New Streaming ASR Model Is the Boring Multilingual Agent Component Everyone Eventually Needs

The most important model releases are usually not the ones that make the best demo reel. They are the ones that remove a piece of glue code from a production system. NVIDIA’s new Nemotron 3.5 ASR Streaming Multilingual 0.6B is that kind of release: a 600 million-parameter

NVIDIA’s Ineffable Intelligence Deal Says the Next AI Bottleneck Is Experience, Not Data

NVIDIA’s Ineffable Intelligence Deal Says the Next AI Bottleneck Is Experience, Not Data

NVIDIA’s partnership with Ineffable Intelligence is easy to dismiss as another frontier-lab funding story with better lighting. Do not stop there. The interesting part is not the valuation, the founder résumé, or the phrase “superlearners.” It is the workload NVIDIA is trying to prepare for: AI systems that generate

DeepSeek V4 on NVIDIA Blackwell Is a Long-Context Agent Story Disguised as an Inference Post

DeepSeek V4 on NVIDIA Blackwell Is a Long-Context Agent Story Disguised as an Inference Post

DeepSeek V4 is being sold as a million-token model launch. That is the least interesting way to read it. NVIDIA’s new guidance for running DeepSeek V4 on Blackwell, GPU-accelerated endpoints, NIM, vLLM, SGLang, NemoClaw, AI-Q, and NeMo is really a serving story. The model is large enough to get

The Best Local Coding-Agent Post Today Says the Quiet Part: The Model Is a Worker, Not the Authority

The best local coding-agent post today does not ask whether Qwen 3.6 is smart enough to write code. It asks the more useful question: what should the model be allowed to decide? Manolo Remiddi’s writeup on building a local coding-agent setup with Qwen 3.6, OpenCode, Hermes, and

A 100B-Class NVFP4 Quant on 2× DGX Spark Is the Most Useful Kind of Messy Benchmark

A 100B-Class NVFP4 Quant on 2× DGX Spark Is the Most Useful Kind of Messy Benchmark

The most useful benchmark posts are usually the messy ones. A fresh NVIDIA Developer Forum thread on quantizing a 100B-class Llama-derived model to NVFP4 across two DGX Spark systems is valuable because it does not sand off the parts operators actually trip over: unified-memory misdetection, exporter quirks, vLLM sidecar fixes,

Hermes on DGX Spark Shows Local Agents Are Becoming a Hardware Product — With a Reliability Bill Attached

Hermes on DGX Spark Shows Local Agents Are Becoming a Hardware Product — With a Reliability Bill Attached

NVIDIA’s latest RTX AI Garage post is not really about one more agent framework. It is about local agents becoming a hardware product. Hermes Agent, Qwen 3.6, RTX PCs, RTX PRO workstations, and DGX Spark are being bundled into a single story: keep the agent near your files,

NVIDIA and SAP Are Turning Agent Security Into Runtime Infrastructure, Not Policy Theater

NVIDIA and SAP Are Turning Agent Security Into Runtime Infrastructure, Not Policy Theater

Enterprise AI agents are finally running into the wall every production engineer knew was coming: the prompt is not a security boundary. NVIDIA and SAP’s SAP Sapphire announcement matters because it treats agent safety as runtime infrastructure, not governance theater wrapped around a chatbot. That sounds less glamorous than

DGX Spark’s Nemotron 3 Super Benchmark Is Useful Because It Measures Stability, Not Just Speed

DGX Spark’s Nemotron 3 Super Benchmark Is Useful Because It Measures Stability, Not Just Speed

The most useful number in the latest DGX Spark Nemotron benchmark is not 23.45 tokens per second. It is zero. Zero crashes. Zero out-of-memory errors. A same-evening NVIDIA Developer Forum post reports Nemotron-3-Super-120B-A12B-NVFP4 running on a single DGX Spark at 23.45 tokens/sec for a tg128 single-session test,

Nemotron 3 Nano Looks Fast on Jetson Thor — Until Concurrency Makes the Runtime Tell the Truth

Nemotron 3 Nano Looks Fast on Jetson Thor — Until Concurrency Makes the Runtime Tell the Truth

Jetson Thor can run serious local AI models now. That is no longer the interesting part. The interesting part is that a same-day NVIDIA Developer Forum benchmark shows exactly where the glossy edge-AI story starts paying rent: concurrency, runtime support, kernel selection, and parser glue. A user tested NVIDIA’s

NVIDIA’s Wan2.2 FP8/NVFP4 Checkpoints Are the Boring Part of Video AI That Actually Matters

NVIDIA’s Wan2.2 FP8/NVFP4 Checkpoints Are the Boring Part of Video AI That Actually Matters

The least glamorous part of generative video is becoming the part that matters: not whether the demo clip looks impressive, but whether the model can be served, measured, rolled back, and kept inside a cost envelope without turning every request into a GPU bonfire. NVIDIA’s new FP8 and NVFP4