Introducing the
Quasar
A foundation model built to handle long, consistent context.
Join the frontier of decentralized reasoning.
Backed by the best
Quasar Foundation Models
Introducing Quasar, a revolutionary series engineered to shatter the context window limitation. With millions of tokens in a single context, Quasar enables reasoning across vast datasets without forgetting a single detail.
Context Window Comparison
99.9% Recall
Perfect retrieval across millions of tokens.
Linear Scaling
Consistent performance at any depth.
1/10th Cost
Optimized attention reduces inference overhead.
The Context Bottleneck
Traditional Transformers have a quadratic complexity problem ($O(N^2)$). Doubling the context length quadruples the compute cost. This makes long-context reasoning prohibitively expensive and slow.
Linear Attention
Complexity Reduction
Technical Specs
Training Efficiency
By leveraging decentralized compute on Bittensor, Quasar reduces training costs by orders of magnitude compared to centralized labs.
Built on Bittensor
We leverage the $TAO network to orchestrate a global, decentralized training run. Intelligence is no longer centralized; it is mined, verified, and incentivized in real-time.
Quasar Subnet Mining
In this adversarial environment, independent Miners train high-performance variations of the Quasar architecture. They don't just process static data; they compete to solve complex reasoning tasks across a spectrum of sequence lengths.
Sequence Competition
Models are challenged on inputs ranging from 8k to 2M tokens. Miners must optimize for both retrieval accuracy and inference speed to survive the network's rigorous selection process.
Validator Consensus & Rewards
Validators act as the immutable source of truth. They continually audit miner checkpoints against unseen datasets. Top-performing models are automatically rewarded with $TAO, creating a self-sustaining cycle of improvement.
LongBench
Language Modeling
Evaluates standard language modeling capabilities across diverse long-context datasets, ensuring scalability and coherence.
QuasarBench
Synthetic Needle
Strict synthetic "needle-in-a-haystack" testing. Verifies absolute retrieval accuracy across the full context window.
Narrative Understanding
Ingest entire book series. Quasar doesn't just summarize; it remembers every character arc, plot twist, and subtle detail.
What happened in the 3rd book?
In Prisoner of Azkaban, Harry discovers Sirius Black is his godfather.
Full Codebase Analysis
Feed Quasar your entire repository. trace bugs across modules, refactor legacy patterns, or generate comprehensive documentation.
Where is the authentication logic?
Auth logic is in auth.ts (lines 45-120).
Legal & Financial Review
Twice the accuracy. Instantly spot contradictions, track liability clauses across documents, and synthesize insights from the entire corpus.
Conflicting liability clauses?
Contract A caps liability at $1M; Contract C implies unlimited.
Meet the Team
Leading SILX with deep expertise in AI research and visionary thinking to guide our mission towards the future of synthetic intelligence.
Model Weights
Access our open-source model weights on Hugging Face. Download and deploy Quasar for your own applications.
Quasar-2M-Base
Foundation model optimized for long-context understanding and reasoning tasks
All models are released under open-source licenses. Join our community on Hugging Face to collaborate and contribute.