Analysis: Attention ISN’T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique

Market Context & Signal Intelligence

When the transformer architecture was introduced in 2017 in the now seminal Google paper “Attention Is All You Need,” it became an instant cornerstone of modern artificial intelligence. Every major large language model (LLM) — from OpenAI’s GPT series to Anthropic’s Claude, Google’s Gemini, and Meta’s Llama — has been built on some variation of its central mechanism: attention, the mathematical operation that allows a model to look back across its entire input and decide what information matters most.Eight years later, the same mechanism that defined AI’s golden age is now showing its limits. Attention is powerful, but it is also expensive — its computational and memory costs scale quadratically

with context length, creating an increasingly unsustainable bottleneck for both research and industry. As models aim to reason across documents, codebases, or video streams lasting hours or days, attention becomes the architecture’s Achilles’ heel.On October 28, 2025, the little-known AI startup Manifest AI introduced a radical alternative. Their new model, Brumby-14B-Base, is a retrained variant of Qwen3-14B-Base, one of the leading open-source transformer models.But while many variants of Qwen have been trained already, Brumby-14B-Base is novel in that it abandons attention altogether. Instead, Brumby replaces those layers with a novel mechanism called Power Retention—a recurrent, hardware-efficient architecture that stores and updates information over arbitrarily long contexts without the exponential memory

growth of attention.Trained at a stated cost of just $4,000, the 14-billion-parameter Brumby model performs on par with established transformer models like Qwen3-14B and GLM-4.5-Air, achieving near-state-of-the-art accuracy on a range of reasoning and comprehension benchmarks.From Attention to Retention: The Architectural ShiftThe core of Manifest AI’s innovation lies in what they call the Power Retention layer. In a traditional transformer, every token computes a set of queries (Q), keys (K), and values (V), then performs a matrix operation that measures the similarity between every token and every other token—essentially a full pairwise comparison across the sequence. This is what gives attention its flexibility, but also what makes it so costly:

processing a sequence twice as long takes roughly four times the compute and memory.Power Retention keeps the same inputs (Q, K, V), but replaces the global similarity operation with a recurrent state update. Each layer maintains a memory matrix S, which is updated at each time step according to the AI financial blind spot Google Watch & Learn framework CRM for business growth

Strategic Response Playbook

• Business Automation focus: map how Attention ISN’T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique influences investments in business automation, and convert qualitative signals into dashboards that every revenue leader can act on.
• Workflow Automation focus: map how Attention ISN’T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique influences investments in workflow automation, and convert qualitative signals into dashboards that every revenue leader can act on.
• Crm Integration focus: map how Attention ISN’T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique influences investments in crm integration, and convert qualitative signals into dashboards that every revenue leader can act on.
• Sales Pipeline focus: map how Attention ISN’T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique influences investments in sales pipeline, and convert qualitative signals into dashboards that every revenue leader can act on.
• Cross-functional governance: build a shared backlog that sequences quick wins, integration milestones, and change-management narratives so teams adopt the automation playbook with confidence.

Execution Priorities for Growth Teams

1. Stand up a discovery sprint that inventories current automation assets, data contracts, and integration constraints, capturing the gaps that automation-led business consultancy with technical and growth-first mindset teams experience daily.
2. Define measurable hypotheses for customer journey improvements, then align marketing, sales, and success operations around shared instrumentation to test those hypotheses in live campaigns.
3. Architect an enablement cadence that threads together playbooks, dashboards, and retrospectives so the organization internalizes a clear, insightful, strategic with actionable depth execution rhythm.
4. Establish a reinforcement loop: executive sponsors review progress monthly, recalibrate priorities, and ensure funding remains tied to clear revenue or efficiency outcomes.