Thinking Machines Lab has released the model weights for Inkling 2 Small under Apache 2.0. The 594-billion-parameter mixture-of-experts model was pretrained with native NVFP4 arithmetic on NVIDIA Blackwell hardware, processing 8.4 trillion tokens in 68 hours. It activates 17 billion parameters per token and scores 31 on the Artificial Analysis Intelligence Index, up from 13 for the original Inkling Small.

The technical significance comes from using four-bit floating-point operations during pretraining instead of converting a higher-precision checkpoint after training. The release notes provide evidence that low-precision training can support a large sparse model while reducing memory traffic and increasing tensor-core throughput on compatible hardware. Actual cost savings depend on cluster size, utilization, communication overhead and the higher-precision states retained by the training recipe.

594B weights, 17B at work

Inkling 2 Small combines sparse expert routing, hybrid attention, image input and an advertised one-million-token context window. Its DSpark speculator accelerates generation by drafting tokens for the main model to verify.

A mixture-of-experts model loads its full checkpoint across the available devices while routing each token through a small subset of experts. Inkling therefore has the storage and communication demands of a 594B checkpoint, even though its per-token expert computation is closer to a 17B model.

FP4 enters pretraining

Many low-bit releases begin as BF16 checkpoints and are quantized after training. Thinking Machines instead used NVIDIA’s NVFP4 format during pretraining, including the model’s principal forward and backward matrix operations.

NVFP4 represents values with four-bit floating-point numbers and shared scales for small groups of values. Those scales preserve more useful range than plain four-bit integers. Compared with FP8, four-bit operands can halve storage for quantized tensors and double peak tensor-core throughput for supported Blackwell operations.