Search: “inference”

Together AI provides the inference stack behind both: high-throughput serving on the latest NVIDIA Blackwell GPUs for agentic workloads, and TensorRT engines plus event-driven streaming I/O for low-la

Day 2 at #MSBuild is about what it takes to move beyond generic foundation models. Think customization, inference performance, and getting production-ready AI deployed at scale. @chahvivi will lead a

Fine-tuning to production inference is the gap where teams get stuck. At #MSBuild today, our own Rob Ferguson, @danielhanchen (@UnslothAI) and @marksaroufim (@coreautoai) discuss: model customization

Using open models and inference clouds (which serve open models) is a leading indicator of what is to come. The advantage of open weights is that you can train, serve, and continually improve your own

Move from test to production by running high-performance inference directly on Foundry. At #MSBuild, we demoed an end-to-end workflow showing how unified infrastructure improves latency, reduces cost,

Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This kee

MiniMax M3 is live and Together AI is powering its inference 🚀 Tomorrow at 6pm PT we're going live on X Spaces with the teams behind the model and the infrastructure to give you a deep dive. https://x

what a wonderful project: parakeet.cpp https://github.com/mudler/parakeet.cpp GGML based parakeet inference pipeline that's 2x faster than my ONNX parakeet pipeline on Apple Silicon! (Needed a few loc

Having Grok Build sub-agents to iterate several ideas for me on data loading, batching, inference, and writing results to files for dense datasets before I went to sleep. It gave me a nice summary of

Every LLM API call depends on the inference engine underneath it. Tokenization, scheduling, prefill, decode, KV cache, batching, and streaming determine whether the experience is fast, scalable, and p

glad to know Mythos' safety concerns have been addressed right as Anthropic also secured tens of billions in inference compute 👍 JUST IN: Anthropic announces it will roll out Claude Mythos “in the com

Elon Musk indicated that the next development phase involves implementing an inference stack in C (with some C++) to enable high-speed reinforcement learning operations simultaneously across a large c

i own 0 A100s but i can run cloud inference with sub 80ms network latency by renting one in Kansas (0% action queue starvation) this is an early checkpoint of smolvla trained on 50 episodes for ~20k s

Super happy that a bunch of people are finding marlin useful. Thank you for the inference support @ZeroGPU_AI @huggingface 🥰🤝 We’ve got hands on more compute now so we’ll also release a series of blog

Train with autoregression & convert weights to diffusion for inference. Most researchers agree that autoregression is best when memory bandwidth is cheap and diffusion is best when FLOPS are cheap. Th

Our inference stack, optimized for Blackwells, with a novel attention kernel and many new optimizations has started rolling out! It's already charting on Artificial Analysis, eg: #1 speed and latency

MTP means Multi Token Prediction. It's a speculative decoding technique that can result in large inference speedups in many cases. 1. Update to LM Studio 0.4.14 2. Download a model that supports MTP l

LLMs are stateless (every time you reply to an LLM, you re-inject the entire conversation to a fresh inference) the purpose of memory is to provide continuity (in games we'd call it a 'persistent worl

⚡️After weeks of hard work, we're thrilled to fully open-source HY World 2.0 today -- full inference code and all models! Build, explore, and create your own interactive worlds with us.👇 https://githu

Teams like @modal are already using Devin Auto-Triage for incidents on their inference team. “Devin Automations feels like a step forward from other auto-triage tools we’ve tried. It monitors our chan

Heading to hashtag#MLSys2026? Come unwind with the Together AI team at Inference After Dark. Drinks, bites, shuffleboard, and a room full of researchers and AI-native builders. 🟠 Tuesday, May 19 🟠 7:3

Pearl generates proofs from matrix multiplications that already happen during training and inference computations. Those proofs help secure Pearl Network, and the future value of Pearl emissions helps

great agents need great infrastructure. proud to be @LangChain's Deep Agents Inference Partner at Interrupt 2026 in SF. great to spend time with builders at our day 1 happy hour. excited for day 2 tod

Training a diffusion LLM from scratch is challenging, and diffusion inference advantages don't show up at training. So we convert instead. Building on the TiDAR recipe, we take ZAYA1-8B-base and run d

What we find most useful about TST is the decoupling. The training-time efficiency is fully separated from the inference-time architecture, which makes TST a clean addition on top of other pretraining

This NVIDIA remains the strongest platform for large-model inference at scale. Prefill/decode disaggregation, Blackwell-native quantization, custom kernels, and rack-scale NVLink turn GB200 into faste

Open-source language models have reached sufficient quality and capability levels just as commercial frontier model providers have begun increasing their inference API pricing. This timing creates a p

Apple dominate local inference 6/10 This is completely opposite to enterprise/data-center adoption where Nvidia is king. Huge market Nvidia is letting slip. High VRAM + mid bandwidth + working kernels

Learn how to structure your AI stack. Our co-founder @the_bunny_chen talks with @theinfrapod's @tnachen and @ianlivingstone about inference optimization, the open vs. closed model debate, and building

Welcome to DS4, a specialized inference engine for DeepSeek v4 Flash. https://github.com/antirez/ds4 This project would have been impossible without the existence of llama.cpp and GGML and the work of

inference >>> Ramp's monthly Top Software Vendors is one of the best signals for where to work, where to invest, or who to sell to. Here are May's breakout + fastest growing vendors. We observe that @

Introducing TokenSpeed, a speed-of-light LLM inference engine. > TensorRT LLM level performance > vLLM level usability > Built by a lean and mission-driven team in two months > MIT license, open-sourc

Why are four of the top 10 trending vendors AI inference platforms? Companies are getting selective about which model handles which task, and routing tokens accordingly. Read more from @arakharazian a

Excited to introduce Gemma 4 Multi-Token Prediction Drafters⚡️Accelerated inference right in your pockets - Up to a 3x speedup - Same quality guarantees - Available in your favorite open-source tools

Inference is 80-90% of the lifetime cost of a production AI system. Most AI-native teams are leaving performance and margin on the table. Here’s how Together AI, the AI Native Cloud, fixes that on @nv

MiniMax-M2.7 is now available across six inference providers on Artificial Analysis, with significant differentiation in speed and price @SambaNovaAI leads on speed at 435 output tokens/s, >3x faster

🤯 Ollama now supports Claude Desktop via Claude’s built-in third party inference. ollama launch claude-desktop This allows all models from Ollama's Cloud to be used across Claude Cowork and Claude Cod

With this integration, teams can optimize data, launch fine-tuning, evaluate results, and deploy on Together AI inference through a tighter workflow. Learn more: http://www.together.ai/blog/announcing

As models, contexts, and workloads grow, hidden assumptions in inference infrastructure can surface as output anomalies. Reliability requires more than throughput, latency, and availability. It also r

Our cofounder @the_bunny_chen joined @GregorVand to talk about open models, production inference, and how RFT is unlocking model customization for teams without a dedicated ML org. They covered custom

The DeepSeek V4 garbled output bug in open source inference engine is fixed in SGLang. To everyone affected over the weekend, sorry for the trouble. Huge thanks to @Ant_Group for landing the fix PR. I

DeepSeek v4 Flash with *local inference* after 24h of playing with that: even with the 2 bit selective quantization GGUF, iti is the FIRST time I feel I have a frontier model running on my computer. T

2 bit DeepSeek v4 Flash inference. Experts w1/3: IQ2_XXS, w2: Q2_K, all the rest left mostly F16/F32 Final GGUF: 86.18 GiB Runs on a MacBook M3 max with CPU (No Metal backend test as it will crash my

Inference that never sleeps, for agents that never stop. 'Why cowork when you can delegate?' That's @DhruvBatra_ on @yutori_ai's new Delegate — an always-on agent that monitors, researches, and acts a

In ChatGPT, full-stack inference improvements enable a more capable model at faster speed. This efficiency is a game-changer for GPT-5.5 Pro, now a much more practical option for demanding tasks, and

Excited to share our work on production-ready W4A8 inference, now integrated in vLLM! By combining 4-bit weights (low memory) with 8-bit activations (high compute), we hit the sweet spot for both deco