Juice Labs

⚡ Edge GPU acceleration is gaining serious momentum — projected to reach $65B by 2035. ⚡ The big shift? As edge inferencing use cases come into focus, Network Service Providers are realizing edge GPUs aren’t just for AI they’re positioned to transform traditional GPU workloads like CAD, CAE, and media rendering for engineers and creators. At Juice Labs, we virtualize and pool GPUs across metro and edge locations so latency-sensitive GPU workloads run like they’re on local workstations without the cost, heat, or refresh cycles of high-powered GPU workstations. Technical Impact: ✅ Millisecond latency   → Edge GPUs feel like in-chassis compute   ✅ Dynamic GPU allocation   → No more over-provisioned GPU workstations sitting idle    ✅ Cross-environment orchestration   → Bare metal, containers, VMs tapping the same GPU fabric For developers, engineers, and IT teams, this means: ⚡ GPU-intensive apps at the edge with workstation-level responsiveness ⚡ Centralized GPU management ⚡ Lower TCO across the board ⚡ Turn CPU-only workstations to GPU powered workhorses When a GPU is <5ms away, the old model of static GPU workstations becomes optional. #EdgeAI #GPUvirtualization #LowLatency #NSP #Juice #GPUpower #EngineeringInnovation #CAD #MediaRendering #EdgeComputing

𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗳𝗮𝗰𝗲 𝗮 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: 𝗗𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗚𝗣𝗨 𝗮𝗰𝗰𝗲𝘀𝘀 𝗳𝗮𝗿 𝗼𝘂𝘁𝗽𝗮𝗰𝗲𝘀 𝘀𝘂𝗽𝗽𝗹𝘆. Undergrad Computer Science students often get locked out of meaningful hands-on experience because GPU clusters are prioritized for graduate research. The result? Idle GPUs and frustrated students who miss the chance to build practical AI skills early. 𝗪𝗶𝘁𝗵 𝗝𝘂𝗶𝗰𝗲, 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗰𝗮𝗻: ⚡ Pool GPUs across labs, classrooms, and research groups ⚡ Dynamically allocate fractional or full GPUs at runtime ⚡ Improve ROI by maximizing utilization (no need to overbuy for peak) ⚡ Equitable access so underclass students gain AI skills from day one 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲: Better GPU economics, broader student access, and a stronger pipeline of AI-ready graduates. Universities shouldn’t have to choose between research and education. With Juice, they can deliver both. 👉 Learn more: Juice Labs #HigherEducation #AI #GPU #Efficiency #JuiceLabs #EdTech #innovation #education #AcademicResearch

𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗳𝗮𝗰𝗲 𝗮 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: 𝗗𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿 𝗚𝗣𝗨 𝗮𝗰𝗰𝗲𝘀𝘀 𝗳𝗮𝗿 𝗼𝘂𝘁𝗽𝗮𝗰𝗲𝘀 𝘀𝘂𝗽𝗽𝗹𝘆. Undergrad Computer Science students often get locked out of meaningful hands-on experience because GPU clusters are prioritized for graduate research. The result? Idle GPUs and frustrated students who miss the chance to build practical AI skills early. 𝗪𝗶𝘁𝗵 𝗝𝘂𝗶𝗰𝗲, 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝗶𝗲𝘀 𝗰𝗮𝗻: ⚡ Pool GPUs across labs, classrooms, and research groups ⚡ Dynamically allocate fractional or full GPUs at runtime ⚡ Improve ROI by maximizing utilization (no need to overbuy for peak) ⚡ Equitable access so underclass students gain AI skills from day one 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲: Better GPU economics, broader student access, and a stronger pipeline of AI-ready graduates. Universities shouldn’t have to choose between research and education. With Juice, they can deliver both. 👉 Learn more: Juice Labs #HigherEducation #AI #GPU #Efficiency #JuiceLabs #EdTech #innovation #education #AcademicResearch

⚡ Juice’s software GPU fabric unlocks AI adoption in manufacturing ⚡ 🏭 AI is reshaping manufacturing from visual inspection and robotics to simulation and predictive maintenance. But in a typical setup, a $10K+ GPU sits idle any time its host has no active workload. Teams overprovision to avoid delays, but that increases capex and complexity. Juice tackles these problems head-on with a virtualized software-only GPU fabric that runs across any environment - on-prem, edge, or hybrid – with no other changes to your setup. With Juice: ✅ GPUs are decoupled from hosts and pooled centrally ✅ Workloads can dynamically access fractional GPU slices ✅ VM, container, and bare-metal compatibility is built-in ✅ No code changes are needed Resulting in: 📊 2-3x throughput per GPU 💰 Up to $300K saved per site, per year ⚙️ 30-50% faster time-to-deploy AI use cases 🌐 Real-time inference closer to the edge Static GPU deployments limit innovation. Juice gives manufacturers the flexibility to scale AI workloads efficiently, from vision systems to predictive maintenance and robotics. #AI #Manufacturing #GPU #Virtualization #EdgeComputing #HybridCloud #JuiceLabs #SmartFactory #HPC

Can’t see your GPU utilization? 👀 You’re probably bleeding money, power, and productivity. Many teams have no easy way to see which GPUs are idle, underused, or overloaded. Most tools are challenging to deploy and leave gaps in visibility, especially when trying to manage across workstations, edge, and data center deployments. With just a simple install (no custom scripts, no deployment headaches) Juice gives you a comprehensive view of your GPU utilization. Whether GPUs reside in workstations, the cloud, or on-prem infrastructure, Juice Labs helps you identify underutilized GPUs and target them for additional work. ⚡Simple install ⚡Complete visibility ⚡Smarter sharing & slicing ⚡Higher utilization ⚡Increased ROI #GPU #AIInfrastructure #HPC #Virtualization #JuiceLabs

Most enterprise and academic GPU workstations are underutilized. Juice lets you pool and share that power across your network to increase productivity. Try it for yourself! #GPUVirtualization #GPUUtilization #HPC #AIInfrastructure #AcademicComputing #EnterpriseIT #TechProductivity #EdgeComputing #JuiceLabs #DigitalTransformation

Pool and share GPUs across systems with the Juice Desktop App Most GPU workstations are underutilized. Juice virtualizes GPUs over standard networking so you can pool and share GPU compute across your network, boosting creativity and making your team more productive. 👇 Get started! (Link in first comment)

⚡ Juice is a master class in mixing and maximizing your GPUs ⚡ Tired of rebooting servers just to reallocate GPU resources? Frustrated with the complexity of vGPU, MIG, or Kubernetes-based solutions? Juice is changing the game for DevOps and system admins: * Dynamic GPU slicing at runtime: no restarts, Kubernetes optional * Pool and share GPUs across nodes  * Run on workstations, in the data center, or across both * Access GPUs remotely over standard IP:  accelerated compute when and where you need it * HPC-friendly with Slurm support * User friendly desktop app * Install in under 60 seconds Whether you’re managing a local AI stack, scaling inference across a cluster, or juggling hybrid workloads, Juice helps you get the most out of your GPU infrastructure. Built for the real world. Trusted by teams who move fast. https://www.juicelabs.co #DevOps #SysAdmin #GPUVirtualization #JuiceLabs #GPUInfrastructure #GPUoverIP #RuntimeSlicing #HybridCloud #Slurm #EdgeAI #NoKubernetes #WorkstationReady

⚡Virtualize H100 fleets to maximize your AI Edge⚡ An NVIDIA H100 is capable of generating nearly 100 million tokens per day - but across the industry, these GPUs sit idle ~70% of the time. Traditional virtualization can’t keep up with the scale and velocity of modern AI - contributing to rising hardware and energy costs and underutilized multi-million-dollar GPU infrastructure. Juice drives your token economics in the right direction:               ⚡90%+ utilization of existing H100s (and other GPUs too) ⚡2-3x cost reduction across hardware, power, and rack space ⚡Near-local performance for LLMs, ML training, and inference workloads Juice turns your stranded H100 capacity into strategic advantage. Let's talk. https://www.juicelabs.co/ #H100 #AIInfrastructure #GPUOptimization #Virtualization #CIO #CTO #AIAcceleration #JuiceLabs #DataCenterEfficiency

⚡ Attention Quant CIOs / CTOs running GPUs ⚡ There's a way to unlock ~60% more GPU capacity - without buying more hardware. Quant firms are sitting on (something you will soon turn into) gold - underutilized GPUs. At Juice, we see firms operating with <15% token-level utilization due to the old model of fixed GPU-to-Quant ratio.  That’s over 1 trillion tokens wasted per A100 GPU per day. With Juice, you can: ✅ Dynamically allocate GPU power based on real-time need ✅ Postpone expensive GPU purchases until target utilization is achieved ✅ Reduce total GPU footprint by up to ~60% while maintaining the same level of productivity With no code changes. Your existing infrastructure - finally working at full capacity. 🔗 Learn how: https://www.juicelabs.co