Epoch AI published a manufacturing teardown of NVIDIA's B200 last month.
Jensen Huang called it a "horrible outcome for America.
Scroll through the investor list on SiFive's freshly closed 400 million round and you hit a name that shouldn't be there: NVIDIA.
Google just told you its biggest cost problem isn't training. It's serving.
Quantum computing has a plumbing problem.
Jensen Huang does not spend $20 billion on chips he could build himself. He spends it on chips he did not want to admit he needed.
NVIDIA's latest MLPerf Inference v6.0 submission hit $0.
NVIDIA just dropped a full-stack open robotics platform during National Robotics Week, and it's the first time I've looked at humanoid robot...
Meta just signed the single largest AI hardware procurement deal among the Magnificent Seven, and the check went to AMD, not NVIDIA.
Anthropic just told us where the inference money is going, and it's not where most people expected.
For the first time in the MLPerf inference benchmarks, AMD posted numbers that don't require mental gymnastics to interpret.
NVIDIA dropped Nemotron 3 Super a few weeks ago, and the discourse moved on within 48 hours. Understandable — March was a firehose of model releases.
Three years ago, running a GPT-4-class model cost roughly 20 per million tokens. Today the same caliber of output runs at 0.
Twelve months ago, if you asked an ML platform team what kept them up at night, the answer was GPU availability.
NVIDIA dropped Nemotron 3 Super a few weeks ago and it flew under the radar — buried by the Mythos leak drama and GPT-5.4's benchmark parade.
Q1 2026 delivered more custom inference silicon than any quarter in history. Google deployed Ironwood.
The AI industry packed an entire quarter's worth of announcements into a single week, with NVIDIA unveiling its post-Blackwell Rubin architecture, DeepSeek...