Baseten

Baseten introduced the Baseten Environment Network, handing the construction of reinforcement-learning environments for post-training to a set of vetted partner companies. Previously Baseten's own applied research team built these environments by hand for post-training engagements such as Harvey, Lovable and Gamma, work the company describes as manually intensive and dependent on researchers' judgment. Partners work directly with customers and build environments from the customer's real data, while Baseten handles training and inference deployment; Baseten also plans to open-source a set of frontier-quality environments along with how it audits and trains on them.

Baseten introduced the Baseten Environment Network, handing the construction of reinforcement-learning environments for post-training to a set of vetted partner companies. Previously Baseten's own applied research team built these environments by hand for post-training engagements such as Harvey, Lovable and Gamma, work the company describes as manually intensive and dependent on researchers' judgment. Partners work directly with customers and build environments from the customer's real data, while Baseten handles training and inference deployment; Baseten also plans to open-source a set of frontier-quality environments along with how it audits and trains on them.

Baseten and Postman announced a partnership that splits enterprise AI agent work in two: Postman's Agent Stack prepares APIs, controls which systems agents can reach and governs their actions, while Baseten supplies high-performance inference, multi-cloud GPU capacity and post-training. Baseten says its model APIs placed in the top two providers on Artificial Analysis for throughput and latency across seven 2026 launches, and cites Harvey post-training a 27B open model to match closed-source frontier performance on its legal agent benchmark.

Baseten and Postman announced a partnership that splits enterprise AI agent work in two: Postman's Agent Stack prepares APIs, controls which systems agents can reach and governs their actions, while Baseten supplies high-performance inference, multi-cloud GPU capacity and post-training. Baseten says its model APIs placed in the top two providers on Artificial Analysis for throughput and latency across seven 2026 launches, and cites Harvey post-training a 27B open model to match closed-source frontier performance on its legal agent benchmark.

Baseten moved the parent copy of its delta weight sync back onto the GPU, cutting a roughly 770 GB GLM-5.3 checkpoint sync from 36 seconds with a 6-second request pause to 15 seconds with a 0.8-second pause, and raising throughput by as much as 3.2x. The earlier design kept the full parent in host memory, which on an 8×B300 node could not coexist with a 1.1 TB host KV cache, so the cache shrank to 275 GB. The new path reads only changed GPU positions and verifies updates with delta checksums before writing.

Baseten moved the parent copy of its delta weight sync back onto the GPU, cutting a roughly 770 GB GLM-5.3 checkpoint sync from 36 seconds with a 6-second request pause to 15 seconds with a 0.8-second pause, and raising throughput by as much as 3.2x. The earlier design kept the full parent in host memory, which on an 8×B300 node could not coexist with a 1.1 TB host KV cache, so the cache shrank to 275 GB. The new path reads only changed GPU positions and verifies updates with delta checksums before writing.

Base Labs released joey-1, a 2B decision model that answers label-style calls such as routing a ticket or flagging a message by returning a single token and a calibrated probability in one forward pass, instead of generating text. At production scale it serves the average 2,000-token request at a P50 end-to-end latency of 20 ms, about 2.5x faster than elephant-1 at 49 ms, and at $0.01 per million tokens it costs a tenth of mammoth-1.

Base Labs released joey-1, a 2B decision model that answers label-style calls such as routing a ticket or flagging a message by returning a single token and a calibrated probability in one forward pass, instead of generating text. At production scale it serves the average 2,000-token request at a P50 end-to-end latency of 20 ms, about 2.5x faster than elephant-1 at 49 ms, and at $0.01 per million tokens it costs a tenth of mammoth-1.

Baseten's research lab released two open-source decision models, mammoth-1 (27B) and elephant-1 (9B), which return a probability distribution over every possible answer instead of generating text. mammoth-1 ranks first among 208 decision models on the Jev Decision Index 0.4, while elephant-1 is tied for eighth there and is the leading 9B decision model, and is tied for first with d1-ultra on the separate Artificial Analysis Decision Index. Both were post-trained from Qwen3.5-9B and Qwen3.5-27B on 6.21 million decision-making examples, are free to try for a week on Baseten Model APIs, and are open-source on Hugging Face.

Baseten's research lab released two open-source decision models, mammoth-1 (27B) and elephant-1 (9B), which return a probability distribution over every possible answer instead of generating text. mammoth-1 ranks first among 208 decision models on the Jev Decision Index 0.4, while elephant-1 is tied for eighth there and is the leading 9B decision model, and is tied for first with d1-ultra on the separate Artificial Analysis Decision Index. Both were post-trained from Qwen3.5-9B and Qwen3.5-27B on 6.21 million decision-making examples, are free to try for a week on Baseten Model APIs, and are open-source on Hugging Face.

Baseten now lets read-only model data be published once as a sealed volume: a directory or checkpoint is committed as an immutable version addressed by a content digest that any deployment can mount. Previously the same checkpoint was downloaded and re-uploaded to several places, each deployment holding its own config pointing at a copy, so a mismatched revision surfaced only when outputs diverged. Volumes are chunked and keyed by content hash, so a push transfers only changed chunks, and a mount makes files available as the container starts, removing the pull phase from startup. Sealed Volumes ship with Baseten CLI 1.1.0 and newer.

Baseten now lets read-only model data be published once as a sealed volume: a directory or checkpoint is committed as an immutable version addressed by a content digest that any deployment can mount. Previously the same checkpoint was downloaded and re-uploaded to several places, each deployment holding its own config pointing at a copy, so a mismatched revision surfaced only when outputs diverged. Volumes are chunked and keyed by content hash, so a push transfers only changed chunks, and a mount makes files available as the container starts, removing the pull phase from startup. Sealed Volumes ship with Baseten CLI 1.1.0 and newer.

NVIDIA released Nemotron 3.5 Super VL, an open multimodal model now available for inference and training on Baseten, so agents can read documents, interpret application screens and analyze video directly. It builds on the Nemotron 3 Super architecture as a 120B-parameter Mixture-of-Experts model that activates 12B parameters per token, adding a vision encoder for image input. In NVIDIA's reported evaluations it scores an Elo of 1190 on GDPval versus 493 for Nemotron 3 Super, and also scores higher on SciCode and GPQA-diamond.

NVIDIA released Nemotron 3.5 Super VL, an open multimodal model now available for inference and training on Baseten, so agents can read documents, interpret application screens and analyze video directly. It builds on the Nemotron 3 Super architecture as a 120B-parameter Mixture-of-Experts model that activates 12B parameters per token, adding a vision encoder for image input. In NVIDIA's reported evaluations it scores an Elo of 1190 on GDPval versus 493 for Nemotron 3 Super, and also scores higher on SciCode and GPQA-diamond.

Runway and Baseten are putting the Gen-4.5 and Aleph 2.0 video models into single-tenant enterprise environments. Previously enterprises could only reach these models through Runway Creative or the Runway Dev API, not run the weights inside their own isolated compute. Enterprises can license weights directly from Runway, run them on Baseten Dedicated Inference, and fine-tune on their own data; Baseten states a 99.99% uptime commitment for such dedicated deployments.

Runway and Baseten are putting the Gen-4.5 and Aleph 2.0 video models into single-tenant enterprise environments. Previously enterprises could only reach these models through Runway Creative or the Runway Dev API, not run the weights inside their own isolated compute. Enterprises can license weights directly from Runway, run them on Baseten Dedicated Inference, and fine-tune on their own data; Baseten states a 99.99% uptime commitment for such dedicated deployments.

Gradium TTS pushes time-to-first-audio below typical human speech response, reaching a median TTFA of 214 ms on the Coval benchmark as of September 2026 with an interquartile range tight enough for developers to set reliable latency budgets for the rest of the pipeline. It uses Kyutai's Delayed Streams Modeling, which treats text and audio as two parallel streams on a shared clock so audio can start before the upstream model finishes the sentence. On Baseten it ships as a dedicated deployment whose weights are pre-mirrored and cached, cutting cold starts by 2–3x.

Gradium TTS pushes time-to-first-audio below typical human speech response, reaching a median TTFA of 214 ms on the Coval benchmark as of September 2026 with an interquartile range tight enough for developers to set reliable latency budgets for the rest of the pipeline. It uses Kyutai's Delayed Streams Modeling, which treats text and audio as two parallel streams on a shared clock so audio can start before the upstream model finishes the sentence. On Baseten it ships as a dedicated deployment whose weights are pre-mirrored and cached, cutting cold starts by 2–3x.