The economics of a Neolab. A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem. To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW. Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining. If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model. For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference. Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money. Add to that insane cost of talent. So what can you do with the compute? - Not play the model game at all. - Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing - Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry. If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend. It is a difficult game.

We’ve spent the past year building high-throughput labs and infrastructure for large-scale physics experiments in our search for better superconductors and magnets. We ran our first experiments by hand. Now, experiments run continuously in the labs. We’re building AI to make sense of what they produce and to determine which experiments to run next.

Our engineers just shipped the fastest named entity recognition (NER) inference on the market: 1 ms P50 and 3 ms P99 server-side latency, 7.7x faster than an optimized PyTorch baseline. The bottleneck in fast NER is rarely the model: it's tokenization, HTTP parsing, networking, and other overhead. We solved for that and added it to Baseten Embeddings Inference (BEI). Just point it at your model checkpoint, no model changes needed.

Anthropic just released Claude plugins for financial workflows. However, no one knows how well it works. We are putting together the first skills hackathon to test out how skills work for different domains. If you are interested RSVP in the comment 👉🧵

tldr: We open-source a inference engine that deduplicates prefill tokens and wrote a paper (@juliuslipp). RadixMLP was missed chance by the community that developed varlen (THD-packed) inference, and overlooked by people working on training and inference engines. [1/x]

Claude Code also encourages oversight by stopping to ask questions. On complex tasks, Claude Code pauses for clarification more than twice as often as humans interrupt it. Training models to recognize uncertainty is an important, under-appreciated safety property.

Introducing Tinker: a flexible API for fine-tuning language models. Write training loops in Python on your laptop; we'll run them on distributed GPUs. Private beta starts today. We can't wait to see what researchers and developers build with cutting-edge open models! thinkingmachines.ai/tinker