#llamastack — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #llamastack, aggregated by home.social.
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As LLMs move into production, #Observability is essential for Reliability, Performance & Responsible AI.
Learn how to deploy an #opensource observability stack - using Prometheus, Grafana, Tempo, and OpenTelemetry Collectors on Kubernetes - and monitor real #AI workloads with #vLLM & #Llamastack.
🎥 Watch the #InfoQ video (#transcript included): https://bit.ly/4hlKoDa
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As LLMs move into production, #Observability is essential for Reliability, Performance & Responsible AI.
Learn how to deploy an #opensource observability stack - using Prometheus, Grafana, Tempo, and OpenTelemetry Collectors on Kubernetes - and monitor real #AI workloads with #vLLM & #Llamastack.
🎥 Watch the #InfoQ video (#transcript included): https://bit.ly/4hlKoDa
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3 things to know about Red Hat AI 3
https://www.youtube.com/watch?v=eztORiJWYMs
#RedHat #AI #RedHatAI #llmd #Agentic #MCP #ModelContextProtocol #LlamaStack #OpenSource #OpenShift #OpenShiftAI
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Red Hat Brings Distributed AI Inference to Production AI Workloads with Red Hat AI 3
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Red Hat Brings Distributed AI Inference to Production AI Workloads with Red Hat AI 3
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Introducing vLLM Inference Provider in Llama Stack
https://blog.vllm.ai/2025/01/27/intro-to-llama-stack-with-vllm.html
#artificialintelligence #AI #vLLM #llamastack #opensource #RedHat
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Introducing vLLM Inference Provider in Llama Stack
https://blog.vllm.ai/2025/01/27/intro-to-llama-stack-with-vllm.html
#artificialintelligence #AI #vLLM #llamastack #opensource #RedHat
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🦙 #LlamaStack: Standardizing #GenerativeAI Development
Defines open API specs for #AI application building blocks
Covers full lifecycle: model training, evaluation, production deployment
Includes APIs for inference, safety, memory, agents, and more
Supports multiple environments: local, hosted, and on-device
🛠️ Features:
#OpenSource API providers and distributions
Mix-and-match capabilities (e.g., local small models, cloud-based large models)
Consistent APIs across platforms (server, mobile, etc.)
🤝 Supported implementations:
API Providers: #Meta Reference, #Fireworks, #AWS Bedrock, #Together, #Ollama, TGI, #Chroma, PG Vector, #PyTorch ExecuTorch
Distributions: Meta Reference, Dell-TGI
📦 Easy installation via pip or from source 🖥️ Includes 'llama' CLI for managing distributions, models, and more
Learn more: https://github.com/meta-llama/llama-stack
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🦙 #LlamaStack: Standardizing #GenerativeAI Development
Defines open API specs for #AI application building blocks
Covers full lifecycle: model training, evaluation, production deployment
Includes APIs for inference, safety, memory, agents, and more
Supports multiple environments: local, hosted, and on-device
🛠️ Features:
#OpenSource API providers and distributions
Mix-and-match capabilities (e.g., local small models, cloud-based large models)
Consistent APIs across platforms (server, mobile, etc.)
🤝 Supported implementations:
API Providers: #Meta Reference, #Fireworks, #AWS Bedrock, #Together, #Ollama, TGI, #Chroma, PG Vector, #PyTorch ExecuTorch
Distributions: Meta Reference, Dell-TGI
📦 Easy installation via pip or from source 🖥️ Includes 'llama' CLI for managing distributions, models, and more
Learn more: https://github.com/meta-llama/llama-stack