Microtensor @Microtensor_

𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗯𝘂𝗶𝗹𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀. At 𝗠𝗶𝗰𝗿𝗼𝘁𝗲𝗻𝘀𝗼𝗿, we turn raw AI capability into a 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗼𝗳 𝗱𝗲𝗱𝗶𝗰𝗮𝘁𝗲𝗱 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁𝘀 that fit seamlessly into the 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗲. The smartest way to run AI in your business isn't to lean on generic, oversized models. 𝗜𝘁'𝘀 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻. We bridge the gap between frontier intelligence and the 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 that actually run your company. By mapping 𝘁𝗮𝘀𝗸-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗺𝗼𝗱𝗲𝗹𝘀 to your day-to-day operations, Microtensor lets your teams automate everyday work 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝘆, 𝗽𝗿𝗶𝘃𝗮𝘁𝗲𝗹𝘆, 𝗮𝗻𝗱 𝗮𝘁 𝗮 𝗳𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗰𝗼𝘀𝘁. 𝗡𝗼 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝗵𝗮𝘂𝗹𝘀. 𝗡𝗼 𝗯𝗹𝗼𝗮𝘁𝗲𝗱 𝗔𝗣𝗜 𝗯𝗶𝗹𝗹𝘀. Discover the smartest way to run AI in your business. Book a demo 👉 microtensor.cloud/sales #Microtensor #AI #Bittensor bittensor:native #Subnet #EnterpriseAI #WorkflowAutomation #TechInfrastructure

Running LLMs in production takes more than calling a model. Teams have to decide which model handles each request, keep that model's behaviour stable as it changes, and process large volumes without failures going unnoticed. In practice, that means writing and maintaining their own routing logic, version tracking, retry queues and logging, just to know which model answered, how well, and at what cost. Microtensor solves this with three tools built into a single drop in inference layer. Product teams can hand off routing, versioning and bulk processing, and get predictable performance with clear quality and cost data on every request. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗿𝗼𝘂𝘁𝗶𝗻𝗴 The need: deciding which model should handle each request. How: swap the base URL in your OpenAI client and send `mt/auto`. Requests matching a certified task go to a certified specialist, and everything else goes to a general model. What you get: every response reports the task detected, the specialist that answered, its confidence, and whether it met your quality floor. 𝗣𝗶𝗻𝗻𝗲𝗱 𝗲𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 The need: model behaviour that changes under a live product. How: lock a named endpoint to the specialist versions you choose, each with its own quality bar, content log and traffic. What you get: behaviour that stays the same until you upgrade. When a better specialist is certified, you see the accuracy change and upgrade with one click. 𝗕𝗮𝘁𝗰𝗵 𝗿𝘂𝗻𝘀 The need: running thousands of requests without building queues and retries. How: upload a CSV, JSONL or TXT file of up to 50,000 requests and run it through routed inference or a pinned endpoint. What you get: every row comes back with the specialist and version, quality score, tokens and latency, plus a summary against the estimated cost on a frontier model. Docs: microtensor.cloud/docs/api/getti… #AIInfrastructure #SmallLanguageModels #Developers #Bittensor bittensor:native #Subnets

How the listening device got installed at OpenAI

microtensor.cloud/docs/api/getti…

Most AI workloads are over provisioned. Teams route almost every request to a frontier model. Fixing a typo and debugging a checkout failure run on the same parameter heavy network, at the same latency and the same premium price. For the bulk of production traffic, that capacity goes unused. At Microtensor, we are building a task calibrated inference API to close that gap. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 • Drop in: change the base URL and API key in the OpenAI client you already use. No new SDK. • Identify: a lightweight, calibrated router classifies each request by task, such as classification, decisions or structured extraction. • Serve: a certified specialist model, fine tuned for that task, answers the request. Only when the router's confidence falls below your quality floor does the request escalate to a frontier model. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝘀𝗵𝗼𝘄𝘀 • Specialists close the gap. An adapted small model recovered 89.7% of frontier performance at 4% of the cost (Carnegie Mellon University). • Routing preserves quality. Quality aware routing cut calls to the large model by up to 40% with no drop in response quality (Hybrid LLM, ICLR 2024). • Smaller footprint. Task specific models use orders of magnitude less energy per query than general purpose generative models (Luccioni, Jernite and Strubell, ACM FAccT). The result: frontier accuracy where the task demands it, and specialist cost, speed and efficiency everywhere else. Coming soon. Follow our progress at microtensor.cloud #AI #SmallLanguageModels #LLM #AIInfrastructure #Developers #Bittensor bittensor:native #Subnets

Last week the Microtensor team rolled out a complete platform redesign for a fresh look and feel, and upgraded our entire miner mechanism to make model certification more rigorous and fairer for miners and validators. 🛠️✨ This week our focus shifts to user facing tools, making sure our end users can put certified specialists to work from day one. We're starting with an OpenAI compatible inference API with task aware routing. Why now? In recent weeks OpenAI launched GPT 6 Astra, xAI launched Grok 4.7, Anthropic launched Claude Opus 5.5 and Google announced Gemini 4 Argon. Deciding which model is the smartest is no longer the real problem. The real question is which model should handle this particular task. 🧠 "Fix a typo" and "debug our checkout failure" sit at opposite ends of the difficulty spectrum. Yet most systems send both to massive generalist models, paying frontier inference costs for simple tasks that a compact specialist resolves at a fraction of the compute. Here's what's coming to Microtensor: 1️⃣ Drop in compatibility: point the OpenAI client you already use at Microtensor. One API key, no new SDK to learn. 2️⃣ Task identification: a calibrated router reads each incoming request and classifies the task, whether text classification, a critical decision or structured data extraction. 3️⃣ Specialist inference: each task is served by the certified specialist best suited for it, escalating to a frontier model only when the router's confidence falls below your quality floor. 🎯 The result? Frontier level accuracy, lower latency and a fraction of the inference cost, just by switching the base URL and key in your existing code. Stay tuned. The future of decentralised inference is lean, smart and efficient. 🚀 microtensor.cloud #Bittensor #Subnet bittensor:native #AIInfrastructure #SLMs #LLMs #Developers

🔹 Frontier models only when needed: sending easy queries to a small model and escalating hard ones cut large model calls by up to 40% with no quality drop (Hybrid LLM, ICLR 2024), and matched GPT 4 at up to 98% lower cost (FrugalGPT, Stanford). Frontier level accuracy on your task. A fraction of the cost, energy and carbon. Running where you choose. That is the case for specialised SLMs, and it is what Microtensor delivers. Before you scale, calculate what every query costs. Our whitepaper: microtensor.cloud/research

🔹 Less energy per query: task specific models are orders of magnitude cheaper to run than general purpose generative models, even at equal size (Luccioni, Jernite and Strubell). The hungriest frontier LLMs use over 29 Wh per long prompt, 65x the most efficient (Jegham et al.). 🔹 Independence: a specialist fits on a single GPU, so it can run on your own infrastructure. When Moxie's servers shut down, the robots that kept working were the ones owners moved onto open source software they ran themselves (PIRG)

In December 2024, families who paid $799 for Moxie, an AI companion robot for kids, learned it was about to stop working. Every conversation ran on large language models in the cloud (OpenAI API). When the funding ran out, the servers went dark (Axios). 🧵 Moxie is an extreme case, but the lesson applies to every AI product. When every interaction depends on a large model in someone else's cloud, inference cost becomes the cost of your business. As one report put it, "LLMs are not cheap to run" (Aftermath) axios.com/2024/12/10/mox…

@DocumentingTao @lium_io

@MiraaCrypto @opentensor In the US/UK, adoption hits walls like HIPAA or financial privacy laws that block sending data to public cloud APIs. In regions like Africa or Asia, the policy is similar but limited by cross-border bandwidth to Western data centers

bittensor:native The next wave of AI adoption will not be decided where AI is already abundant. In the US and UK, putting AI into a hospital or a bank means clearing layer after layer of regulation. Growth is moving to Africa, Asia and Latin America. @opentensor #Bittensor #Subnets

@ledgersable @opentensor Good question @ledgersable. Validation and serving are separate. Validators certify each system once on reference hardware, anywhere on the network. The certified model runs locally, even offline, so new users in a region add no validation load. It scales with tasks, not users.