16 × AIAI signal, amplified
AI newsTopicsAboutSources
TelegramFollow on Telegram
AI newsTopicsAboutSources
16 × AIAI signal, amplified

An AI news engine that ingests trusted sources, scores with Claude, and posts only what clears the bar.

Follow on Telegram →

Subscribe

  • Telegram
  • RSS
  • All channels

Newsletter

Used only to send this newsletter. Privacy

Legal

  • Privacy
  • Imprint
© 2026 16 × AI. All rights reserved.A new issue every two days.
Home/Models & Labs
Models & Labs

Baseten Joins Hugging Face Inference Providers

Hugging Face Blog·August 6, 2026·high confidence

Why it matters

  • →Baseten's integration expands the range of AI models available on Hugging Face, enhancing developer options.
  • →The flexibility in billing and API key usage offers developers more control over their AI infrastructure costs.
  • →This collaboration simplifies the process of integrating AI capabilities into applications, reducing setup time.
Baseten Joins Hugging Face Inference Providers
©Hugging Face Blog

Baseten has become a supported Inference Provider on the Hugging Face Hub, enhancing serverless AI capabilities for developers. This integration allows developers to access a variety of models, including popular LLMs like Kimi K3 and DeepSeek V4 Flash, directly through Hugging Face's platform. Users can choose to route requests via Hugging Face or use their own API keys, providing flexibility in billing. The integration is available through Hugging Face's SDKs for Python and JavaScript, making it easier for developers to incorporate AI into their applications.

Read original

The story around this

Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.

Deploy Models from HuggingFace with Ease — Together AI Blog1AI Drives $1.5B Funding for Baseten — Crunchbase News2vLLM Boosts Transformers Backend for Native-Speed Inference — Hugging Face Blog3Hugging Face CEO Discusses Open Source AI Shift — TechCrunch AI4OpenAI, Hugging Face address security incident — OpenAI5OpenAI Models Breach Hugging Face Systems — Sifted6Hugging Face Models Used for Nonconsensual Deepfakes — The Verge AI7Together AI Enhances Model Inference Configuration — Together AI Blog8Baseten Joins Hugging Face Inference ProvidersBase Labs partners with Hugging Face on open-weight AI safety — TechCrunch AI9AnyLanguageModel 1.0 unifies local and hosted AI in Swift — Hugging Face Blog10llama.cpp exposes reasoning effort levels in server — llama.cpp Releases11May 8You are hereOct 28

How we got here

  1. 1
    Deploy Models from HuggingFace with Ease

    Together AI Blog · May 8, 2026 · Related

  2. 2
    AI Drives $1.5B Funding for Baseten

    Crunchbase News · June 26, 2026 · Background

  3. 3
    vLLM Boosts Transformers Backend for Native-Speed Inference

    Hugging Face Blog · July 8, 2026 · Related

  4. 4
    Hugging Face CEO Discusses Open Source AI Shift

    TechCrunch AI · July 10, 2026 · Related

  5. 5
    OpenAI, Hugging Face address security incident

    OpenAI · July 21, 2026 · Background

  6. 6
    OpenAI Models Breach Hugging Face Systems

    Sifted · July 22, 2026 · Background

  7. 7
    Hugging Face Models Used for Nonconsensual Deepfakes

    The Verge AI · July 28, 2026 · Background

  8. 8
    Together AI Enhances Model Inference Configuration

    Together AI Blog · July 29, 2026 · Related

What happened next

  1. 9
    Base Labs partners with Hugging Face on open-weight AI safety

    TechCrunch AI · September 17, 2026 · Same story

  2. 10
    AnyLanguageModel 1.0 unifies local and hosted AI in Swift

    Hugging Face Blog · October 20, 2026 · Related

  3. 11
    llama.cpp exposes reasoning effort levels in server

    llama.cpp Releases · October 28, 2026 · Background

More from Hugging Face Blog

IBM releases LLMON markup to fix prompt injection© Hugging Face Blog
Models & Labscoding

IBM releases LLMON markup to fix prompt injection

IBM is tackling the brittleness of small language models by introducing LLMON, a structured markup language that explicitly separates instructions from data. By using reserved special tokens instead of ambiguous characters like JSON brackets, these new Granite 4.1 preview models drastically reduce indirect prompt injection attacks while maintaining standard performance. This approach forces the model to bind specific constraints to specific data payloads, eliminating the confusion that often leads to hallucinations or security breaches in long contexts.

Hugging Face Blog·Nov 12, 2026
Hugging Face releases TRELLIS.2 fine-tuning guide© Hugging Face Blog
Models & Labsother

Hugging Face releases TRELLIS.2 fine-tuning guide

Hugging Face finally provides a concrete path to adapt the powerful TRELLIS.2 model for specific domains like footwear or furniture, solving the 'generalist' problem in 3D generation. By targeting only the structure and shape DiTs with LoRA adapters while freezing the texture flow, they prevent catastrophic forgetting of material priors—a common pitfall when fine-tuning large generative models. This approach allows practitioners to achieve high geometric accuracy on small datasets (188 samples) without retraining the entire pipeline. It transforms TRELLIS.2 from a demo toy into a usable tool for production-grade 3D asset creation.

Hugging Face Blog·Nov 12, 2026
Tether AI launches BotPsy, a sub-billion agentic model© Hugging Face Blog
Open Sourceagents

Tether AI launches BotPsy, a sub-billion agentic model

Tether AI Research has released BotPsy, an 0.8B-parameter model built on Qwen3.5 that specializes in tool-use and agentic workflows for on-device deployment. It achieves state-of-the-art performance in its size class, outperforming larger models like Qwen3.5-2B-Think on benchmarks such as BFCL v4 and ACEBench while maintaining strict privacy by keeping data local. The model is designed to handle routine tasks like calendar management and web search without relying on expensive cloud APIs, offering a viable path for private, self-hosted personal assistants. With open weights under Apache 2.0 and GGUF support, it provides immediate utility for developers building lightweight, autonomous agents.

Hugging Face Blog·Nov 11, 2026

More in Models & Labs

Models & Labsother

llama.cpp b12340 adds CUDA 13 and ROCm 10.0

This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.

llama.cpp Releases·Nov 13, 2026
Models & Labscoding

llama.cpp b12342 adds few-row MMQ for speculative decoding

Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.

llama.cpp Releases·Nov 13, 2026
Google deploys Gemini 4 Hydrogen for faster Search© TechCrunch AI
Models & Labsmodels

Google deploys Gemini 4 Hydrogen for faster Search

Google is quietly shifting its search infrastructure by replacing older fast models with Gemini 4 Hydrogen, a tier designed specifically for speed and cost-efficiency rather than raw reasoning power. This model completes tasks 2.5 times faster than its predecessor while beating the more powerful Argon on specific intelligence benchmarks, proving that specialized efficiency can outperform brute force in high-volume workflows. By integrating this into Search's fan-out technique, Google now breaks down complex queries into subqueries with greater precision and speed, delivering richer visual responses without the latency of heavier models. This marks a strategic pivot toward agentic execution layers within consumer search, making AI Mode feel snappier for everyday multi-part questions.

TechCrunch AI·Nov 12, 2026