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Beam (Beam) is an open source serverless platform for AI apps. Run GPU inference, background jobs, and sandboxes with ultrafast boot times and no vendor lock-in.

11,750 views • 6 months ago •via X (Twitter)

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#mixtral #mistral #LLM360 Serving Mixtral and LLM360 on FEDML Nexus AI ( We offer Mixtral model endpoints the cheapest in the market: only $0.0005 / 1K tokens! FEDML embraces open source and open model weights. We believe the future of AI belongs to large-scale open collaboration. Today we are excited to support new advances in open-source foundation models: Mixtral, the latest open-source LLM beating Llama2-70B with Mixture-of-Experts (MoE) architecture, and Amber and CrystalCoder backed by LLM360, the framework for open-source LLMs to foster transparency, trust, and collaborative research. Compared to existing fragmented ML products in the market, FEDML Nexus AI is the next-gen cloud service for LLM and Generative AI. It provides an end-to-end platform backed by serverless/decentralized AI infrastructure. Specifically: 1. Economical Serving Engine, ScaleLLM, is where you run your model in cheaper price by optimizing GPU memory and with fully optimized throughput for supporting more concurrent requests. 2. FEDML® Deploy simplifies CLI and MLOps workflow for model deployment on a serverless GPU cloud or on-premise cluster. 3. Serverless Endpoint runs on serverless GPU clouds. With our pay per use policy, we abstract the responsibility of acquiring or leasing an extensive GPU inventory when your are uncertain about your future AI service traffic. The autoscaling feature seamlessly adjusts the backend GPU resources in response to your service traffic. 4. On-premise Deployment helps you own your LLM model on your local environment with AI safety support. 5. FEDML® Launch for serverless GPU clouds. With one-line CLI, it swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, abstracting complex environment setup and management. 6. Zero-code Fine-tuning supported by FEDML® Studio optimizes your model on your domain-specific data without writing any line of source code. 7. Pre-training LLM supports cluster management and experimental tracking. You maintain your training clusters for your urgent needs in your vertical domain. As a closing note, FEDML is gearing up to unveil a cutting-edge service for LLM-based agents and our own cost-effective LLM. Please stay tuned and keep an eye out for upcoming announcements!

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No single vendor will win the AI race, but open ecosystems might. Real velocity in AI comes from interoperability, not lock-in. And AMD just made all of its software open source. At last week’s Advancing AI 2025, we sat down with AMD’s VP of AI Software Anush Elangovan and Sharon Zhou VP of AI at AMD, to discuss their case for why an open, multi-partner ecosystem will accelerate AI innovation faster than any proprietary alternative. AMD’s announcements last week double down on this OSS focus and their commitment to AI infrastructure, including: ✅ Open Source Ecosystem: ROCm 7, AMD’s latest open-source AI software stack, introduces kernel-level improvements for GEMM operations, optimized attention mechanisms, and expanded support for distributed inference. The update brings substantial speedups for inference workloads, with average performance increases of 3.2x to 3.8x ✅ Hardware: New MI355X GPU delivers up to 40% more tokens per dollar vs competition & the MI350 Series has seen a 35x generational leap in AI inference performance ✅ Infrastructure Investments: Oracle just committed to zettascale (‼️) clusters with up to 131,072 MI355X GPUs and AMD showcased their new $10 billion partnership with Saudi Arabian AI firm HUMAIN to build AI infrastructure, including data centers, powered by AMD chips. ✅ Partnership Momentum: 7 out of 10 top AI companies now run production workloads on AMD Instinct accelerators (including Meta, OpenAI, Microsoft & xAI) By inviting interoperability and contribution at every layer, AMD is enabling developers to build faster, optimize deeper, and deploy with flexibility. Listen to Anush and Sharon’s Chain of Thought Podcast episode with host Conor Bronsdon in the next tweet to get all the details and a deep dive into AMD’s strategy 👇

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