AMD just dropped Hyperloom – a multi‑agent harness that auto‑tunes inference on Instinct GPUs using ROCm, vLLM, SGLang & xDiT. Faster, smarter AI pipelines without the manual grind. Dive in to see how it works! #AMDHyperloom #GPUInference #InstinctGPUs

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Cut your AI token cost in half! NVIDIA’s AI Grid slashes inference price 52.8% vs central and 76.1% at burst. Distributed GPU power meets edge latency tricks. Dive in to see how your models can save big. #NVIDIAAIGrid #GPUInference #EdgeLatency

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AMD just dropped Hyperloom v1.0.0a1 – a new tool that auto‑tunes inference on Instinct and MI GPUs via ROCm. Faster AI workloads without the manual grind. Curious? Check out the details. #Hyperloom #GPUInference #ROCm

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Just tried vLLM and it slashes latency while keeping GPU memory lean. If you’re into open‑source LLMs, this could change how you serve models in Python. Curious? Dive in! #vLLM #OpenSourceLLM #GPUInference

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Running inference on idle GPUs can boost token throughput and cut costs. The team behind continuous batching shows how to tap spot GPU markets with CoreWeave, Lambda Labs, RunPod. Ready to squeeze more out of your hardware? #ContinuousBatching #GPUInference #SpotGPU

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vLLM’s new PagedAttention slashes latency, cranks up GPU inference, and lets you batch continuously for production LLM workloads. Curious how it beats the OpenAI API? Dive in! #vLLM #PagedAttention #GPUInference

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Big news: Nvidia and Meta are teaming up. Jensen Huang says their new GPUs will boost both inference and LLM training, powering the next wave of generative AI. Curious how this will reshape the AI landscape? Dive in. #NvidiaMeta #GPUInference #GenerativeAI

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