Multiverse Computing @MultiverseCompu

🚀 Meet Comet 60B, in collaboration with @nvidia Built from Nemotron 3.5 Super VL: 📉 50% fewer total parameters 🎯 Within 5% of original accuracy 🖼️ Text, image & video Open-source multimodal AI for agentic workloads. 📰 Learn more here: multiversecomputing.com/resources/mult… 👉 Explore the model: huggingface.co/MultiverseComp…

🔍 Reliable AI agents need to connect every claim to the right source. Presented at the Agentic AI Summit at UC Berkeley, our ProvenanceGuard contribution is now merged into @nvidia NVFlow. It detects when AI agents attribute information to the wrong source by breaking answers into verifiable claims, checking their sources and returning verdicts. 🎙️ Hear our ML Engineer Ander Alvarez Sanz discuss it at Build-a-Claw in Barcelona on October 13. 👉 Explore: lnkd.in/eX_pqQuu 🖋️ Register: lnkd.in/e-42Q9ma #AgenticAI #OpenSource

La autonomía tecnológica no depende solo del desarrollo, sino también de la capacidad de escalar. Necesitamos infraestructuras y capital para desarrollar IA y otras tecnologías en Europa. La financiación pública es clave para movilizar la privada. 🗣️@OrusRoman @MultiverseCompu

🤝 Sports Data Campus y Multiverse Computing (@MultiverseCompu ) se alían para acercar la IA avanzada al ecosistema global del fútbol. Tecnología, formación y aplicación real para impulsar nuevas soluciones en rendimiento, scouting, talento y mucho más. ⚽📊 🔗 sportsdatacampus.com/sports-data-ca… #SportsDataCampus #MultiverseComputing #SportsAnalytics

🎙️ Enterprise speech-to-text, powered entirely by CPUs. Together with @HPE and @intel , we have demonstrated high-volume transcription on a single HPE ProLiant DL380 server with two Intel Xeon 6 processors. No GPUs required. Our optimized Whisper Large V3 Turbo delivers: ⚡ ~2× the throughput of the original model on the same hardware 🗒️ Capacity equivalent to ~17,000 hours of audio per day 📉 50% fewer parameters and half the memory footprint 🎯 Word error rate below 3% in our benchmark 🔒 All audio stays within the customer’s own infrastructure. More capacity to transcribe customer calls, build searchable archives and expand quality monitoring, with full control over data. 👉 lnkd.in/eW3BGw6b #MultiverseComputing #CompactifAI #EfficientAI

📊 Compare cost per completed task, success rate, latency and policy compliance. 🔍 Catch lost context, tool misuse and policy drift before deployment. ⚖️ Evaluate @MultiverseCompu and third-party models side by side. Rerun evaluations as new models arrive to assess upgrades and identify opportunities to reduce costs. 2/2

🚀 Introducing Luminary, our AI wind tunnel for enterprise model selection. With 2.9M LLMs available, which one performs best for your business? Luminary simulates hundreds of complete, multi-turn customer conversations, including backend systems and tool calls, to help you choose the right model for your chatbot or agentic system. 👉 lnkd.in/eth6RRE8 #Luminary #EnterpriseAI #AgenticAI 1/2

⚛️ Inside Quasar 1.1 438B: The quantum circuits behind the synthetic data. Quasar 1.1 438B is the first documented model trained with quantum-generated data, produced on a 156-qubit IBM Heron r2 processor in Donostia-San Sebastián. Our researchers replaced eight transformer layers in a separate 30-billion-parameter language model with a quantum neural network module to generate data through two routes: 🔬 IBM quantum hardware provided a proof of generated sentences. 💻 GPU simulations using device-calibrated noise produced 16,500 samples, around 4.2 million tokens. The hardware contribution is experimental, with no demonstrated advantage over a comparable classical approach. It establishes a working path from a quantum device to a production model’s training data. 📖 Learn more: multiversecomputing.com/papers/inside-… #MultiverseComputing #CompactifAI #GenerativeAI

One more thing.... 🏆 The Quasar 1.1 438B Challenge. Put the model to the test and help us improve it by identifying and reporting its flaws. The five people who report the highest number of verified flaws each earn 60M tokens to spend on the CompactifAI API. Break it, document it, send it over 👇 wkf.ms/4xlUWZi

⚛️🇪🇺 Introducing Quasar 1.1 438B, the first AI model using quantum-generated data. Quasar 438B, the best European AI model, just got a quantum upgrade: Part of its healing set was generated by a hybrid quantum language model running on @IBM Quantum System Two in Donostia-San Sebastián, a 156-qubit IBM Heron processor. It is the first time quantum-generated data enters the CompactifAI pipeline. Quantum computing is not a label on this release, it is part of how the model was built. Sharper, less verbose, tuned for agentic work and ready for European deployment: served by a European company incorporated under EU law and developed in line with EU regulatory requirements, including the transparency expectations of the EU AI Act.

Quasar 1.1 438B is available now. 👉 Try it on the CompactifAI API: ow.ly/3bkJ50ZnK5c 📰 Want the full picture? Our technical note covers the quantum-generated healing data, the expert pruning, the verbosity tuning and the refusal steering method, with the open-weights checkpoint it starts from: ow.ly/E86350ZNWGk #AI #LLM #CompactifAI #MultiverseComputing #Quasar438B

What changed in 1.1: 🧠 Healed on a broader dataset: reasoning traces, tool-call sequences and general knowledge. Against Quasar 1.0: HLE +6.2, LCR +6.4, IFBench +4.6 and GPQA +4.3 points. ✂️ 37.6% fewer output tokens. Average response down from 3,322.9 to 2,074.3 across SciCode, HumanEval, GSM8K, TriviaQA and BBH. Less filler, lower serving cost, same answer. ⚖️ Fewer refusals where they were never warranted. Refusal on politically sensitive prompts drops to 41.00%, from 63.75% in Quasar 1.0. On harmful prompts it holds at 93.00% on JailbreakBench, above Quasar 1.0's 92.00%. The safety envelope is untouched.

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🎙️ From quantum physics to more efficient AI. Our co-founder & CSO, @OrusRoman , joins The New Quantum Era podcast to explore: 🧠 Tensor networks and AI model compression. ⚡ Why smaller models matter for edge deployment. 🔬 Our research executing individual LLM layers on quantum hardware. Discover the science behind efficient AI today and what quantum computing could enable next. 🎧 Listen to the episode: newquantumera.com/podcast/quantu… #MultiverseComputing #EfficientAI

2/2 Europe has world-class quantum science. Turning it into global leadership requires: 💶 European capital for €100M+ rounds 🏭 More real-world industrial deployments 🤝 Stronger cross-border collaboration between companies and investors #QuantumComputing #DeepTech

🇪🇺 Yesterday, our co-founder and CEO Enrique Lizaso joined policymakers, researchers and industry leaders at the European Parliament for “Europe’s Quantum Moment: From Lab to Leadership,” organized by MEP @SLagodinsky and European Quantum Industry Consortium (QuIC) 1/2

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Want to look under the hood of Quasar 438B? Our full technical note explains how we built the model from Z.ai’s open-weights GLM-5.2 using quantum-inspired expert pruning, targeted healing, and quantization-aware compression. 📰 Read it here: multiversecomputing.com/papers/inside-…

Quasar 438B, measured: 🧠 Intelligence Index v4.1.1: 43. The highest score of any European model, 13 points ahead of Mistral Medium 3.5 and 5 ahead of Nemotron 3 Ultra, which carries 112B more parameters. ⚡ 500-token response, reasoning included: 15.3s. Only three models in the comparison answer faster, and only one of them scores higher. 📚 AA-LCR long-context reasoning: 75.0. Level with Grok 4.6 (high) and within a point of Claude Opus 5. 💻 Terminal-Bench v2.1: 69.3. Agents working in real terminal environments, 18.7 points ahead of Mistral Medium 3.5. Efficiency is what we build at Multiverse Computing. Quasar brings it to the 400B+ class.

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🚀 Introducing Quasar 438B, the top European AI model. Externally validated by @ArtificialAnlys: in their latest benchmark comparison, Quasar is the strongest-performing European model evaluated. It is the first large model released by Multiverse Computing. 438 billion parameters, English and Spanish, built for enterprise-scale agents, coding and complex multi-step workloads.

☄️ Inside Quasar 438B Quasar 438B is a compressed and specialized model built from GLM-5.2, the open-weights model developed by Z.ai. Using CompactifAI, we reduced each MoE layer from 265 to 148 experts through quantum-inspired joint expert selection. We then applied targeted healing to recover and sharpen coding and agentic behavior, followed by quantization-aware compression to produce FP8 and NVFP4 variants. 📰 Read the technical note: multiversecomputing.com/papers/inside-… 📍Access the model: auth.multiverseapp.ai/sign-up?redire…

Quasar 438B is available now. 👉 Try it on the CompactifAI API (1st Month FREE!): ow.ly/3bkJ50ZnK5c 📷 Full benchmark breakdown: ow.ly/vVLN50ZI9xS More Quasar updates are coming. Stay tuned. #AI #LLM #CompactifAI #MultiverseComputing #Quasar438B