What if 99% of your requests could subsidize themselves with just 1% cache reuse? Processing one NVIDIA report = $3. At scale, you're burning thousands recomputing the same tokens. We built a calculator to prove it: → 1.06% hit rate = break even → 10% hit rate = $33K saved

🎁 The best gift for your engineering team this holiday season? Not another pizza party. 🌟 5-10x lower GPU costs and sub-second latency speed on cached computations. 🚨 Let them know that Tensormesh is giving $100 in credits to test intelligent caching on their actual

💡 AI inference will consume enough energy to power 22% of US households by 2028. Tech giants are throwing $1T+ at bigger data centers. The breakthrough? Making infrastructure work 5-10× smarter through intelligent caching. 📊 Read the full analysis:

🚨 According to @PureStorage , enterprises running large LLMs across repetitive workloads can offload up to 90% of redundant computation. 🚀 We built Tensormesh because we kept seeing the same pattern: brilliant AI products held back by infrastructure costs that scale faster

🚀 We just shipped two game-changers: 1️⃣ Deploy ANY of 300K+ Hugging Face models directly from Tensormesh 2️⃣ Real-time performance monitoring dashboard No more deployment friction. No more performance blind spots. See the cache hit rates & throughput metrics that actually

🤔 Are you attending AWS re:Invent today? If so, stop by the Redis booth at the Venetian to see our presentation on Scaling AI Inference Beyond Single GPU Limits with Redis! If you aren't attending, check out our presentation from the link below 👇 Presentation:

Your GPU bill: $500K/month Your actual utilization: 50% You're paying for compute that sits idle. 😭 While you're burning cash on underutilized GPUs, your competitors are serving 4× more requests on the same hardware. The throughput gap isn't about buying more GPUs. It's

👀 Black Friday deals? We've got something better. $100 in free GPU credits to build your AI app, not another gadget you'll forget about. What you get with Tensormesh: ✅ 5-10× lower inference costs ✅ Sub-ms latency ✅ Effortless scaling No codes. No fine print. Just ship

❤️Your AI model is incredible. 🙃Your inference latency is killing it. Chatbots lose users at 3+ seconds. Fraud detection misses threats in milliseconds. Recommendations break buying flow. We built Tensormesh to solve this: 5-10× lower GPU costs, sub-ms latency. 📖 Read our

Quick poll for AI builders: Which of these is your biggest challenge in running your LLM applications today? #AI #LLM #GPU #Engineer

🚨 The GPU cost crisis in AI inference is real But what if you could cut inference costs by 10× without changing your model architecture? We break it down in our latest blog 👇 🔗 https://t.co/sPYTqr3SnV #AI #GPU #Inference #FinOps #Tensormesh #LMCache

For AI teams tired of choosing between speed and budget. 👉 See how it works:

⚡ That's why we built Tensormesh: *Sub-ms latency for cached queries *5-10× GPU cost reduction *Works with existing frameworks #AIEngineering #AIStartup #AITwitter

⚙️ The real problem: inference treats repeated computations as brand new work. Traditional caching barely helped. We needed smarter routing + automatic reuse. #LLMs #GenerativeAI #OpenAI

💭 "Just throw more GPUs at it" Worst advice anyone has ever followed. More GPUs just meant bigger bills. Not better performance. 🧵 #AI #MachineLearning #ArtificialIntelligence

Tensormesh unveiled and LMCache Joins the PyTorch Foundation! https://t.co/BSrYoHbr0Y #LMCache #tensormesh #PyTorch #PyTorchFdn

🚀 Big news! We’re thrilled to announce that Tensormesh has raised $4.5M in seed funding to push inference efficiency to the next level. A special thanks to @russellbrandom for breaking it down in @TechCrunch today. 👉 Read more: