# groq

Groq is a computing platform for fast, scalable AI inference using its LPU and LPX technologies alongside NVIDIA GPUs.

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## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin

DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin](<https://devfeed.tech/articles/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin-26913.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-nvidia-groq-3-lpx-deterministic-execution-drives-power-efficient-high-interactivity-inference-on-nvidia-vera-rubin/>)

Author: Tanya Lenz

Published: 2026-09-15T16:55:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Groq 3 LPX](<https://devfeed.tech/topics/groq-3-lpx.md>), [LPX](<https://devfeed.tech/topics/lpx.md>), [NVIDIA Vera Rubin](<https://devfeed.tech/topics/nvidia-vera-rubin.md>), [Vera Rubin NVL72](<https://devfeed.tech/topics/vera-rubin-nvl72.md>), [groq](<https://devfeed.tech/topics/groq.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-platform](<https://devfeed.tech/tags/ai-platform.md>), [drive](<https://devfeed.tech/tags/drive.md>), [dsx](<https://devfeed.tech/tags/dsx.md>), [groq](<https://devfeed.tech/tags/groq.md>), [groq-3-lpx](<https://devfeed.tech/tags/groq-3-lpx.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [lpx](<https://devfeed.tech/tags/lpx.md>), [nvidia-vera-rubin](<https://devfeed.tech/tags/nvidia-vera-rubin.md>), [nvl72](<https://devfeed.tech/tags/nvl72.md>), [performance](<https://devfeed.tech/tags/performance.md>), [power-management](<https://devfeed.tech/tags/power-management.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>), [vera-rubin-nvl72](<https://devfeed.tech/tags/vera-rubin-nvl72.md>)

### AI overview

This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis.

### Source excerpt

Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...

## Groq on Endless Compute, Inside Claude's Mind, and GLM-5.2 Open Weights - The Tokenizer Edition #32

DevFeed: [Groq on Endless Compute, Inside Claude's Mind, and GLM-5.2 Open Weights - The Tokenizer Edition #32](<https://devfeed.tech/articles/groq-on-endless-compute-inside-claude-s-mind-and-glm-5-2-open-weights-the-tokenizer-edition-32-18337.md>)

Original publisher: [Read original article](<https://newsletter.artofsaience.com/p/groq-on-endless-compute-inside-claudes>)

Author: Sairam Sundaresan

Published: 2026-06-21T16:45:54Z

Content type: article

Language: en

Sources: [Gradient Ascent](<https://devfeed.tech/sources/gradient-ascent.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Mixture of Experts (MoE)](<https://devfeed.tech/topics/mixture-of-experts-moe.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [groq](<https://devfeed.tech/topics/groq.md>), [Claude](<https://devfeed.tech/topics/claude.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [github](<https://devfeed.tech/tags/github.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>)

### AI overview

The Tokenizer Edition #32 curates AI and machine learning resources covering model interpretability, compute demand, open-weight models, multimodal video processing, speculative decoding, reinforcement learning, agent testing, and tools for cheaper or safer inference.

### Source excerpt

This week's most valuable AI resources

## Groq on Hugging Face Inference Providers 🔥

DevFeed: [Groq on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/groq-on-hugging-face-inference-providers-7281.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inference-providers-groq>)

Author: Ben Ankiel; Hatice Ozen; Célina Hanouti; Lucain Pouget; Simon Brandeis

Published: 2025-06-16T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [groq](<https://devfeed.tech/topics/groq.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [api-keys](<https://devfeed.tech/tags/api-keys.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [groq](<https://devfeed.tech/tags/groq.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [sdks](<https://devfeed.tech/tags/sdks.md>)

### AI overview

Groq is now available as an Inference Provider on the Hugging Face Hub, including model pages and Hugging Face client SDKs for JavaScript and Python. The article describes Groq's LPU technology, its low-latency and high-throughput inference for LLMs, support for open models such as Meta's Llama 4 and Qwen's QWQ-32B, API access, and custom-key or Hugging Face-routed usage options.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Mistral: Are LLMs Commodities Now?

DevFeed: [Mistral: Are LLMs Commodities Now?](<https://devfeed.tech/articles/mistral-are-llms-commodities-now-33433.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2024/07/24/mistral>)

Published: 2024-07-24T09:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [LLMs](<https://devfeed.tech/topics/llms.md>), [AI Strategy](<https://devfeed.tech/topics/ai-strategy.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [anthropic](<https://devfeed.tech/topics/anthropic.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Google](<https://devfeed.tech/topics/google.md>), [bedrock](<https://devfeed.tech/topics/bedrock.md>), [groq](<https://devfeed.tech/topics/groq.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-strategy](<https://devfeed.tech/tags/ai-strategy.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [api](<https://devfeed.tech/tags/api.md>), [availability](<https://devfeed.tech/tags/availability.md>), [aws](<https://devfeed.tech/tags/aws.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [claude](<https://devfeed.tech/tags/claude.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data](<https://devfeed.tech/tags/data.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [groq](<https://devfeed.tech/tags/groq.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llms](<https://devfeed.tech/tags/llms.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>)

### AI overview

This opinion argues that the growing number of capable frontier language models, including Mistral 2 Large, GPT-4o, Llama 3.1, and Claude Sonnet 3.5, makes model selection increasingly resemble a commodity decision. It recommends evaluating cost, availability, and operator trustworthiness, and favors infrastructure providers that do not also train models.

### Source excerpt

Mistral 2 Large is out, and it's right up there with GPT-4o, ...and Llama 3.1, and Claude Sonnet 3.5, and...yeah, there's a lot of them. These "Frontier Models" are starting to look more like commodities. And with that shift, we need to adjust AI strategy to match. There's strong arguments to make for using an operator that doesn't also train models. Read more!