Inception Labs

“We had our generalist model evaluated by artificial analysis and the intelligence score from AA artificial analysis around 40. So it's comparable to GPT, 4.1 nano, cloud haiku, kind of like Close source speed optimized models. It's roughly comparable in terms …”

“That's kind of like the reason diffusion, diffusion language models are much faster compared to autoregressive models. Is that each neural network evaluation doesn't just give you one token, like in the typical autoregressive world, but it's able to Output, es…”

“They've released some benchmark numbers. They seem to be pretty close to the numbers that we were getting with the Mercury Coder back in some, you know, back in early this year.”

“So just like you would normally serve an LLM using a VLLM or SGLang or a Tensor or TLLM, we have built our own inference engine. And so we are supporting production traffic already with our own inference engine. We support continuous batch and quantization.”

“We've been successful in training the first commercial scale diffusion language models. We call this model Mercury.”

“We have a DPO algorithm specialized for diffusion language models.”

“On the inference side, what we're seeing is that diffusion models are much more efficient. We're actually able to Pareto dominate autoregressive models. If you think about the typical trade-off between throughput versus latency, which you kind of like cannot, …”

“I'm pretty optimistic about a future where diffusion models Can become the dominant solution. I've seen it happen before with GANs a few years ago, so I wouldn't be surprised if that's the case also here.”

“So we don't have a plan at the moment to release models or to open source any model.”