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#inference

100 articles

01
Synthesize Realistic 3D Medical Images at Scale to Ship Pre‑Trained Models
High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by …
NVIDIA Developer BlogResearch#inference#coding#local
24d
02
Specialization Beats Scale: A Strategic Variable Most AI Procurement Decisions Overlook
Specialization Beats Scale: A Strategic Variable Most AI Procurement Decisions Overlook When a model’s training history …
Hugging Face BlogResearch#inference#benchmark#training
24d
03
Build real-time voice applications with Amazon SageMaker AI and vLLM
Artificial Intelligence Build real-time voice applications with Amazon SageMaker AI and vLLM Voice agents, live captioni…
AWS Machine Learning BlogInfra#inference#multimodal
26d
04
Announcing OpenAI-compatible API support for Amazon SageMaker AI endpoints
Artificial Intelligence Announcing OpenAI-compatible API support for Amazon SageMaker AI endpoints Today, Amazon SageMak…
AWS Machine Learning BlogInfra#fine-tuning#inference#langchain
26d
05
Cerebras Brings Trillion Parameter Inference to Enterprises with Kimi K2.6 >>
Cerebras is now running Kimi K2.6 — the leading trillion parameter open-weight model — in enterprise customer trials. Wi…
Cerebras BlogInfra#inference#coding
27d
06
# production-serving ( 1 )
vLLM x Novita AI: PegaFlow for Production-Grade External KV Cache ·13 min read TL;DR: In collaboration with Novita AI, P…
vLLM BlogTutorial#inference
28d
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vLLM x Novita AI: PegaFlow for Production-Grade External KV Cache May 18, 2026 · 13 min read TL;DR: In collaboration with Novita AI, PegaFlow integrates with vLLM as an external KV cache service for LLM inference, implemented as a standalone Rust process and connected through the external...
vLLM x Novita AI: PegaFlow for Production-Grade External KV Cache TL;DR: In collaboration with Novita AI, PegaFlow integ…
vLLM BlogInfra#inference
28d
08
Running PyTorch Models on Apple Silicon GPUs with the ExecuTorch MLX Delegate
Featured projects TL;DR: Introducing the ExecuTorch MLX Delegate - The new MLX delegate enables optimized, GPU-accelerat…
PyTorch BlogHardware#inference
28d
09
vLLM and PyTorch Work Together to Improve the Developer Experience on aarch64
Featured projects TLDR: PyTorch 2.11 makes it possible to install CUDA-enabled PyTorch wheels on aarch64 Linux directly …
PyTorch BlogHardware#inference#coding#gpu
28d
10
PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend
PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend engine="transformers" PaddleOCR contin…
Hugging Face Blog#inference#coding
28d
11
# expert-parallelism ( 1 )
Elastic Expert Parallelism in vLLMMay 14, 2026·11 min readExpert parallelism (EP) is a key technique for serving Mixture…
vLLM BlogTutorial#inference
32d
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# elastic-ep ( 1 )
Elastic Expert Parallelism in vLLMMay 14, 2026·11 min readExpert parallelism (EP) is a key technique for serving Mixture…
vLLM BlogTutorial#inference
32d
13
Announcing VeRL-Omni: Easy, Fast, and Stable RL Training for Diffusion and Omni-Modality Models May 14, 2026 · 7 min read We are excited to announce the pre-release of VeRL-Omni, a general reinforcement learning (RL) post-training framework focused on multimodal generative models, built on top of verl and vllm-omni.
Announcing VeRL-Omni: Easy, Fast, and Stable RL Training for Diffusion and Omni-Modality Models We are excited to announ…
32d
14
Elastic Expert Parallelism in vLLM May 14, 2026 · 11 min read Expert parallelism (EP) is a key technique for serving Mixture-of-Experts (MoE) models at high throughput. WideEP deployments (where EP spans many workers) maximize KV cache capacity, enabling...
Elastic Expert Parallelism in vLLM Expert parallelism (EP) is a key technique for serving Mixture-of-Experts (MoE) model…
vLLM BlogInfra#inference
32d
15
How the NVIDIA Vera Rubin Platform is Solving Agentic AI’s Scale-Up Problem
Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic…
NVIDIA Developer BlogAgents#agents#inference#gpu
32d
16
Unlocking asynchronicity in continuous batching
Unlocking asynchronicity in continuous batching TL;DR: we explain how to separate CPU and GPU workloads to get a massive…
Hugging Face BlogTutorial#fine-tuning#inference
32d
17
Generating Beautiful UIs May 08, 2026
With contributions from Sherif Cherfa and Halley Chang There’s an intuitive skepticism we have toward AI-generated work.…
Cerebras BlogTutorial#inference#training
33d
18
Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI
Artificial Intelligence Fine-tune LLM with Databricks Unity Catalog and Amazon SageMaker AI When you fine-tune large lan…
AWS Machine Learning BlogTutorial#agents#fine-tuning#inference
33d
19
Efficient Edge AI on Arm CPUs and NPUs: Understanding ExecuTorch through Practical Labs
Featured projects TL;DR: - ExecuTorch extends the PyTorch ecosystem to deliver local AI inference on constrained edge de…
PyTorch BlogInfra#inference#local
34d
20
How to Eliminate Pipeline Friction in AI Model Serving
The path from a trained AI model to production should be smooth, but rarely is. Many teams invest weeks fine-tuning mode…
NVIDIA Developer BlogTutorial#fine-tuning#inference
34d
21
vLLM Tops the Artificial Analysis Leaderboard May 11, 2026 · 15 min read How vLLM built the leading deployments of DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B.
vLLM Tops the Artificial Analysis Leaderboard How vLLM built the leading deployments of DeepSeek V3.2, MiniMax-M2.5, and…
vLLM BlogResearch#qwen#inference#benchmark
35d
22
How ChatGPT adoption broadened in early 2026
How ChatGPT adoption broadened in early 2026 Q1 data shows consumer adoption growth across inferred gender, age, and geo…
OpenAI BlogResearch#gpt#inference
35d
23
# kernel-fusion ( 1 )
vLLM Tops the Artificial Analysis LeaderboardMay 11, 2026·15 min readHow vLLM built the leading deployments of DeepSeek …
vLLM BlogTutorial#inference
35d
24
# benchmarking ( 1 )
vLLM Tops the Artificial Analysis LeaderboardMay 11, 2026·15 min readHow vLLM built the leading deployments of DeepSeek …
vLLM BlogTutorial#inference#benchmark
35d
25
vLLM Tops the Artificial Analysis Leaderboard May 11, 2026 · 15 min read How vLLM built the leading deployments of DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B.
vLLM Tops the Artificial Analysis Leaderboard How vLLM built the leading deployments of DeepSeek V3.2, MiniMax-M2.5, and…
vLLM BlogResearch#qwen#inference#benchmark
35d
26
Building Blocks for Foundation Model Training and Inference on AWS
Building Blocks for Foundation Model Training and Inference on AWS Figure: Adapted from "AI's Three Scaling Laws, Explai…
Hugging Face BlogHardware#rag#inference#observability
35d
27
# turboquant ( 1 )
A First Comprehensive Study of TurboQuant: Accuracy and Performance ·12 min read TurboQuant, a method for KV-cache quant…
vLLM BlogTutorial#inference
35d
28
"OncoAgent: A Dual-Tier Multi-Agent Framework for Privacy-Preserving Oncology Clinical Decision Support"
"OncoAgent: A Dual-Tier Multi-Agent Framework for Privacy-Preserving Oncology Clinical Decision Support" - user: oncoage…
Hugging Face BlogInfra#agents#inference#local
37d
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Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer
Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices suc…
NVIDIA Developer BlogHardware#inference#training#gpu
39d
30
Introducing Multi-LoRA on Cerebras Inference May 06, 2026
Today, we are launching Multi-LoRA—multi-adapter support for Low-Rank Adaptation—on Cerebras Inference in private previe…
Cerebras BlogTutorial#fine-tuning#inference#training
39d
31
Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans
Artificial Intelligence Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker tr…
AWS Machine Learning BlogTutorial#inference#training
39d
32
Serving Agentic Workloads at Scale with vLLM x Mooncake May 6, 2026 · 10 min read TL;DR: Agentic workloads generate massive shared prefixes that are often recomputed across turns. By integrating Mooncake's distributed KV cache store into vLLM, we achieve 3.8x higher throughput,...
Serving Agentic Workloads at Scale with vLLM x Mooncake TL;DR: Agentic workloads generate massive shared prefixes that a…
vLLM BlogInfra#agents#inference
40d
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# agentic ( 1 )
Serving Agentic Workloads at Scale with vLLM x Mooncake ·10 min read TL;DR: Agentic workloads generate massive shared pr…
vLLM BlogTutorial#agents#inference
40d
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Serving Agentic Workloads at Scale with vLLM x Mooncake May 6, 2026 · 10 min read TL;DR: Agentic workloads generate massive shared prefixes that are often recomputed across turns. By integrating Mooncake's distributed KV cache store into vLLM, we achieve 3.8x higher throughput,...
Serving Agentic Workloads at Scale with vLLM x Mooncake TL;DR: Agentic workloads generate massive shared prefixes that a…
vLLM BlogInfra#agents#inference
40d
35
vLLM V0 to V1: Correctness Before Corrections in RL
vLLM V0 to V1: Correctness Before Corrections in RL TL;DR. vLLM V1 matched our vLLM V0 reference after we fixed four thi…
Hugging Face BlogInfra#inference
40d
36
Case Studies 5/5/2026 Innovative Solutions Rebuilds Enterprise Services Delivery with Fireworks AI
Innovative Solutions, a Tier 1 AWS Premier Partner delivering hundreds of AI-driven services engagements annually, hit a…
Fireworks AI BlogInfra#agents#inference
41d
37
Advancing youth safety and wellbeing in EMEA
Advancing youth safety and wellbeing in EMEA Announcing our European Youth Safety Blueprint and EMEA Youth & Wellbeing G…
OpenAI BlogInfra#inference#safety
41d
38
MoE at Scale: Making Sparse Models Fast on Real Hardware September 03, 2025
In this video we discuss scaling MoE models on modern hardware and address key optimization challenges. If you can’t ope…
Cerebras BlogTutorial#inference#training
41d
39
MoE Math Demystified: What Does 8x7B Actually Mean? October 14, 2025
This video breaks down MoE inference arithmetic and deployment bottlenecks across different hardware setups. If you can’…
Cerebras BlogTutorial#inference#training
41d
40
In-Kernel Broadcast Optimization: Co-Designing Kernels for RecSys Inference
Featured projects TL;DR: - Traditional RecSys inference explicitly replicates shared user embeddings/sequences for every…
PyTorch BlogInfra#inference#embeddings
41d
41
OpenAI president forced to read his personal diary entries to jury
Greg Brockman never wanted to discuss his personal journal in public. But the OpenAI president has been stuck for days d…
Ars Technica AIInfra#inference
41d
42
Capacity-aware inference: Automatic instance fallback for SageMaker AI endpoints
Artificial Intelligence Capacity-aware inference: Automatic instance fallback for SageMaker AI endpoints As organization…
AWS Machine Learning BlogInfra#fine-tuning#inference#multimodal
42d
43
Case Study - Cognition x Cerebras December 10, 2025
Dec 10 2025 Case Study - Cognition x Cerebras The Dawn of Real-Time Coding Agents TL;DR Powered by Cerebras Inference, C…
Cerebras BlogResearch#inference#coding
45d
44
Cyber-Insecurity in the AI Era
Sponsored Cyber-Insecurity in the AI Era Presented byGC Cybersecurity Cybersecurity was already under strain before AI e…
MIT Technology Review#agents#inference
45d
45
Elon Musk Seemingly Admits xAI Has Used OpenAI’s Models to Train Its Own
While testifying on Thursday in federal court, Elon Musk seemed to indicate that his AI lab may have used OpenAI’s model…
Wired AIInfra#gpt#inference
46d
46
SMG: The Case for Disaggregating CPU from GPU in LLM Serving
How It Started: Hitting the GIL Wall at Scale We’ve been running production model serving for many years. When we first …
PyTorch BlogHardware#inference
46d
47
Speed Up Unreal Engine NNE Inference with NVIDIA TensorRT for RTX Runtime
Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and st…
NVIDIA Developer BlogInfra#inference#gpu
46d
48
Cybersecurity in the Intelligence Age
Cybersecurity in the Intelligence Age An action plan for democratizing AI-powered cyber defense. Artificial intelligence…
OpenAI BlogInfra#inference
47d
49
DeepInfra on Hugging Face Inference Providers 🔥
DeepInfra on Hugging Face Inference Providers 🔥 We're thrilled to share that DeepInfra is now a supported Inference Pro…
Hugging Face BlogAPI#inference#multimodal#coding
47d
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Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM Apr 28, 2026 · 7 min read We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM.
Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM We are excited to support the new…
48d
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Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM Apr 28, 2026 · 7 min read We are excited to support the newly released NVIDIA Nemotron 3 Nano Omni model on vLLM.
Run Highly Efficient Multimodal Agentic AI with NVIDIA Nemotron 3 Nano Omni Using vLLM We are excited to support the new…
48d
52
NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart
Artificial Intelligence NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart Today, we are exci…
AWS Machine Learning BlogTutorial#inference#gpu
48d
53
GitHub will start charging Copilot users based on their actual AI usage
GitHub has announced that it will be shifting to a usage-based billing model for its GitHub Copilot AI service starting …
Ars Technica AIOpen Source#inference#coding
48d1 view
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4/27/2026 DeepSeek V4 Pro: Validating Frontier Models For Production
Why we chose correctness over a Day-0 launch DeepSeek V4 Pro is one of the most important open-model releases this year,…
Fireworks AI BlogInfra#fine-tuning#inference
49d
55
Choco automates food distribution with AI agents
Choco automates food distribution with AI agents Using OpenAI APIs, Choco processes millions of orders, reducing manual …
OpenAI BlogInfra#rag#inference
49d
56
Musk and Altman face off in trial that will determine OpenAI's future
A hotly anticipated trial starts this week, where Elon Musk will attempt to prove that OpenAI, under Sam Altman, has aba…
Ars Technica AIInfra#inference
49d
57
DeepSeek V4 in vLLM: Efficient Long-context Attention Apr 24, 2026 · 17 min read A first-principles walkthrough of DeepSeek V4's long-context attention, and how we implemented it in vLLM.
DeepSeek V4 in vLLM: Efficient Long-context Attention We are excited to announce that vLLM now supports the DeepSeek V4 …
vLLM BlogTutorial#inference
52d
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IBM Research uses vLLM at the heart of its RITS Platform
Featured projects TL;DR: vLLM has been critical to democratizing access to our research community to the latest and grea…
PyTorch BlogResearch#inference
52d
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DeepSeek V4 in vLLM: Efficient Long-context Attention Apr 24, 2026 · 17 min read A first-principles walkthrough of DeepSeek V4's long-context attention, and how we implemented it in vLLM.
DeepSeek V4 in vLLM: Efficient Long-context Attention We are excited to announce that vLLM now supports the DeepSeek V4 …
vLLM BlogTutorial#inference
52d
60
Serving the For You feed
24th April 2026 - Link Blog Serving the For You feed. One of Bluesky's most interesting features is that anyone can run …
Simon Willison BlogInfra#inference
52d
61
Figma - MultiAgents April 16, 2026
Everything is easier now. I have been toying around with agent orchestration for a while now. I’m currently running 10-2…
Cerebras BlogTutorial#inference#training
53d
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The State of FP8 KV-Cache and Attention Quantization in vLLM Apr 22, 2026 · 21 min read Long-context LLM serving is increasingly memory-bound: for standard full-attention decoders, the KV cache often dominates GPU memory at 128k+ contexts, and each decode step must read a large...
The State of FP8 KV-Cache and Attention Quantization in vLLM Introduction Long-context LLM serving is increasingly memor…
vLLM BlogHardware#inference#coding
54d
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# fp8 ( 1 )
The State of FP8 KV-Cache and Attention Quantization in vLLM ·21 min read Long-context LLM serving is increasingly memor…
vLLM BlogTutorial#inference
54d
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# kv_cache ( 1 )
The State of FP8 KV-Cache and Attention Quantization in vLLM ·21 min read Long-context LLM serving is increasingly memor…
vLLM BlogTutorial#inference
54d
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The State of FP8 KV-Cache and Attention Quantization in vLLM Apr 22, 2026 · 21 min read Long-context LLM serving is increasingly memory-bound: for standard full-attention decoders, the KV cache often dominates GPU memory at 128k+ contexts, and each decode step must read a large...
The State of FP8 KV-Cache and Attention Quantization in vLLM Introduction Long-context LLM serving is increasingly memor…
vLLM BlogHardware#inference#coding
54d
66
Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron
Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at lea…
NVIDIA Developer BlogInfra#qwen#inference#observability
54d
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Amazon SageMaker AI now supports optimized generative AI inference recommendations
Artificial Intelligence Amazon SageMaker AI now supports optimized generative AI inference recommendations Organizations…
AWS Machine Learning BlogInfra#inference#coding
54d
68
Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch
Artificial Intelligence Cost-effective multilingual audio transcription at scale with Parakeet-TDT and AWS Batch Many or…
AWS Machine Learning BlogTutorial#rag#inference#multimodal
54d
69
Google unveils two new TPUs designed for the "agentic era"
Most of the companies that have fully committed to building AI models are gobbling up every Nvidia AI accelerator they c…
Ars Technica AIHardware#agents#inference#training
54d
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Disaggregated Serving for Hybrid SSM Models in vLLM Apr 21, 2026 · 15 min read Hybrid architectures that interleave Mamba-style SSM layers with standard full-attention (FA) layers — such as NVIDIA Nemotron-H — are gaining traction as a way to combine the linear-time...
Disaggregated Serving for Hybrid SSM Models in vLLM Introduction Hybrid architectures that interleave Mamba-style SSM la…
vLLM BlogInfra#inference#gpu
55d
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# mamba ( 1 )
Disaggregated Serving for Hybrid SSM Models in vLLM ·15 min read Hybrid architectures that interleave Mamba-style SSM la…
vLLM BlogTutorial#inference
55d
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Disaggregated Serving for Hybrid SSM Models in vLLM Apr 21, 2026 · 15 min read Hybrid architectures that interleave Mamba-style SSM layers with standard full-attention (FA) layers — such as NVIDIA Nemotron-H — are gaining traction as a way to combine the linear-time...
Disaggregated Serving for Hybrid SSM Models in vLLM Introduction Hybrid architectures that interleave Mamba-style SSM la…
vLLM BlogInfra#inference#gpu
55d
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Run High-Throughput Reinforcement Learning Training with End-to-End FP8 Precision
As LLMs transition from simple text generation to complex reasoning, reinforcement learning (RL) plays a central role. A…
NVIDIA Developer BlogInfra#inference#training
56d
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Lessons learned from building multi-agent workflows April 16, 2026
I pay my upfront subscription ($200/month), write what I hope is the right prompt (prompt AND context engineer), and wai…
Cerebras BlogTutorial#agents#inference#training
56d
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Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances
Artificial Intelligence Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances As the demand for g…
AWS Machine Learning BlogHardware#qwen#inference#multimodal
56d
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Optimizing Effective Training Time for Meta’s Internal Recommendation/Ranking Workloads
Motivation and Introduction Across the industry, teams training and serving large AI models face aggressive ROI targets …
PyTorch BlogInfra#inference#training
59d
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Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo
Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attribu…
NVIDIA Developer BlogAgents#agents#inference#coding
59d
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Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock
Artificial Intelligence Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock Opti…
AWS Machine Learning BlogTutorial#inference#multimodal#embeddings
59d
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Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference
Artificial Intelligence Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference…
AWS Machine Learning BlogModel#fine-tuning#inference
60d
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vLLM Korea Meetup 2026 Wrap-Up Apr 14, 2026 · 7 min read Hosted by the vLLM KR Community, with support from Rebellions, SqueezeBits, Red Hat APAC, and PyTorch Korea, the vLLM Korea Meetup 2026 was held in Seoul on April 2nd.
vLLM Korea Meetup 2026 Wrap-Up Hosted by the vLLM KR Community, with support from Rebellions, SqueezeBits, Red Hat APAC,…
vLLM BlogInfra#inference
62d
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vLLM Korea Meetup 2026 Wrap-Up Apr 14, 2026 · 7 min read Hosted by the vLLM KR Community, with support from Rebellions, SqueezeBits, Red Hat APAC, and PyTorch Korea, the vLLM Korea Meetup 2026 was held in Seoul on April 2nd.
vLLM Korea Meetup 2026 Wrap-Up Hosted by the vLLM KR Community, with support from Rebellions, SqueezeBits, Red Hat APAC,…
vLLM BlogInfra#inference
62d
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Canopy Labs’ Orpheus TTS is live on GroqCloud
Canopy Labs’ Orpheus TTS is live on GroqCloud In December, we announced support for Canopy Labs’ Orpheus text-to-speech …
Groq BlogInfra#inference
67d
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Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation Apr 7, 2026 · 22 min read TL;DR: Prefill and decode fight over the same GPUs, causing ITL spikes under load. We show how to disaggregate them on a single 8-GPU MI300X node using AMD's MORI-IO connector — achieving 2.5x...
Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation TL;DR: Prefill and decode figh…
vLLM BlogTutorial#inference#coding
69d
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# disaggregation ( 1 )
Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation ·22 min read TL;DR: Prefill an…
vLLM BlogTutorial#inference
69d
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Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation Apr 7, 2026 · 22 min read TL;DR: Prefill and decode fight over the same GPUs, causing ITL spikes under load. We show how to disaggregate them on a single 8-GPU MI300X node using AMD's MORI-IO connector — achieving 2.5x...
Next-Level Inference: Why Your Single-Node vLLM Setup Needs Prefill-Decode Disaggregation TL;DR: Prefill and decode figh…
vLLM BlogTutorial#inference#coding
69d
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The Debate of MCP vs. CLI Centers on Speed April 06, 2026
MCP had a formative year. Then it had a turbulent week. Perplexity CTO Denis Yarats walked on stage at Ask 2026 and anno…
Cerebras BlogTutorial#inference#training
69d
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4/6/2026 Own Your AI: Fireworks Training Preview
Fireworks Training is now in preview: an end-to-end platform for training and deploying frontier models at scale. Three …
Fireworks AI BlogInfra#fine-tuning#inference#training
70d
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Why speed wins: faster inference is about more than just quicker answers–it’s the new path to accuracy February 19, 2026
Feb 19 2026 Why speed wins: faster inference is about more than just quicker answers–it’s the new path to accuracy Watch…
Cerebras BlogTutorial#inference#training
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4/3/2026 Scaling and Optimizing Frontier Model Training
On this page How Fireworks scales frontier model training and offers the broadest set of fine-tunable MoE models on any …
Fireworks AI BlogHardware#fine-tuning#inference#training
73d
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Announcing Gemma 4 on vLLM: Byte for byte, the most capable open models Apr 2, 2026 · 3 min read With the debut of Gemma 4, vLLM introduces immediate support for Google's most sophisticated open model lineup, spanning multiple hardware backends, with first-ever Day 0 support on Google TPUs,...
Announcing Gemma 4 on vLLM: Byte for byte, the most capable open models Elevating Open Models with Advanced Reasoning an…
vLLM BlogHardware#inference
74d
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Announcing Gemma 4 on vLLM: Byte for byte, the most capable open models Apr 2, 2026 · 3 min read With the debut of Gemma 4, vLLM introduces immediate support for Google's most sophisticated open model lineup, spanning multiple hardware backends, with first-ever Day 0 support on Google TPUs,...
Announcing Gemma 4 on vLLM: Byte for byte, the most capable open models Elevating Open Models with Advanced Reasoning an…
vLLM BlogHardware#inference
74d
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Achieving Single-Digit Microsecond Latency Inference for Capital Markets
In algorithmic trading, reducing response times to market events is crucial. To keep pace with high-speed electronic mar…
NVIDIA Developer BlogInfra#inference
74d
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Accelerating Vision AI Pipelines with Batch Mode VC-6 and NVIDIA Nsight
In vision AI systems, model throughput continues to improve. The surrounding pipeline stages must keep pace, including d…
NVIDIA Developer BlogHardware#inference#multimodal#gpu
74d
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NVIDIA Platform Delivers Lowest Token Cost Enabled by Extreme Co-Design
Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost…
NVIDIA Developer BlogHardware#inference#gpu
75d
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Extracting hidden states from vLLM Mar 30, 2026 · 8 min read PR #33736 (included in vllm>=v0.18.0) introduced a new hidden states extraction system to vLLM. This blog post explores the motivation, design, usage, and future direction of this feature, and its...
Extracting hidden states from vLLM PR #33736 (included in vllm>=v0.18.0 ) introduced a new hidden states extraction syst…
vLLM BlogInfra#inference
77d
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Extracting hidden states from vLLM Mar 30, 2026 · 8 min read PR #33736 (included in vllm>=v0.18.0) introduced a new hidden states extraction system to vLLM. This blog post explores the motivation, design, usage, and future direction of this feature, and its...
Extracting hidden states from vLLM PR #33736 (included in vllm>=v0.18.0 ) introduced a new hidden states extraction syst…
vLLM BlogInfra#inference
77d
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Liberate your OpenClaw
Liberate your OpenClaw 🦀 If you've been cut off and your OpenClaw, Pi, or Open Code agents need resuscitation, you can …
Hugging Face BlogHardware#claude#inference#coding
80d
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Partner Spotlight: Armis + Cerebras Enable Teams Build and Secure Software Faster March 27, 2026
Mar 27 2026 Partner Spotlight: Armis + Cerebras Enable Teams Build and Secure Software Faster At Cerebras, we’ve always …
Cerebras BlogTutorial#inference#training
80d
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Jais 2: A Blueprint for Sovereign AI December 09, 2025
Arabic is spoken by more than 400 million people, yet Arabic-centric Large Language Models (LLMs)still lag behind Englis…
Cerebras BlogTutorial#inference#training
81d
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Cerebras is coming to AWS March 13, 2026
The world’s fastest inference is coming to the world’s leading cloud. Today we're announcing that Amazon Web Services is…
Cerebras BlogTutorial#inference#training
81d