Quasar Alpha
Parameters
-
Context Length
128K
Modality
Multimodal
Architecture
Dense
License
Proprietary
Release Date
10 Jan 2026
Knowledge Cutoff
-
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
-
Key-Value Heads
-
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Normalization
-
Activation Function
-
Dimensions
Hidden Dimension Size
-
Number of Layers
-
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
Quasar Alpha
NVIDIA Quasar Alpha represents NVIDIA's entry into frontier AI models, leveraging decades of GPU computing expertise. Features optimized performance on NVIDIA hardware with advanced capabilities in reasoning, generation, and multimodal understanding. Designed for high-performance computing environments with efficient scaling on NVIDIA infrastructure. Early alpha release demonstrating competitive performance on standard benchmarks.
About Quasar
NVIDIA's Quasar Alpha represents frontier AI capabilities leveraging NVIDIA's expertise in GPU-accelerated computing and AI infrastructure. Designed for high-performance applications with optimized inference on NVIDIA hardware.
Other Quasar Models
- No related models available
Evaluation Benchmarks
Rank
#66
| Benchmark | Score | Rank |
|---|---|---|
Coding Aider Coding | 0.55 | 23 |
Rankings
Overall Rank
#66
Coding Rank
#60
Quasar Alpha Model Integrity Report
Total Score
25
/ 100
Audit Note
NVIDIA Quasar Alpha is a highly opaque model characterized by a 'stealth' release strategy that obscures its architectural origins and training data. While it demonstrates strong performance in third-party coding and context benchmarks, the total lack of documentation on parameters, compute, and licensing makes it a 'black box' for developers. The model's identity is inconsistent across platforms, further undermining its transparency profile.
Upstream
Architectural Provenance
NVIDIA Quasar Alpha is described as a 'dense' architecture with 'Multi-Head Attention' and 'Absolute Position Embedding.' However, there is no disclosure regarding the base model or whether it was trained from scratch. Official documentation lacks critical technical details such as the number of layers, hidden dimension size, or specific activation functions. The model is frequently referred to as a 'cloaked' or 'stealth' model in partner distributions like OpenRouter, which intentionally obscures its architectural lineage.
Dataset Composition
There is zero public information regarding the training data sources, composition breakdown, or filtering methodology. NVIDIA's official pages provide only vague marketing claims about 'leveraging decades of GPU computing expertise' without naming a single dataset. No information exists on the ratio of web data, code, or synthetic data used in training.
Tokenizer Integrity
While the model is accessible via API, the specific tokenizer is not publicly released as a standalone tool. Independent testing suggests it may use a vocabulary similar to GPT-4o (tiktoken), but NVIDIA has not officially documented the vocabulary size, tokenization approach, or training alignment for Quasar Alpha specifically.
Model
Parameter Density
The parameter count is officially listed as 'Unknown' or '-' in technical specifications. While the architecture is confirmed as 'dense,' there is no disclosure of total parameters. This lack of transparency makes it impossible to verify efficiency or scaling claims against other frontier models.
Training Compute
No specific compute metrics have been disclosed. While NVIDIA highlights its 'Blackwell' and 'H100' infrastructure in general marketing, the actual GPU hours, hardware count, and carbon footprint for training Quasar Alpha are completely absent from public documentation.
Benchmark Reproducibility
NVIDIA mentions 'competitive performance on standard benchmarks' and some third-party results exist (e.g., Aider Coding at 55%, NoLiMa at 85.1%), but the official evaluation code, exact prompts, and few-shot configurations are not public. The lack of a technical paper or detailed model card prevents independent verification of the claimed scores.
Identity Consistency
The model exhibits significant identity confusion. In various deployments and third-party reports, it has been identified as a 'cloaked' version of other models or has failed to consistently identify itself as an NVIDIA product. This is exacerbated by its distribution as a 'stealth' model, which is a direct violation of transparency regarding model identity.
Downstream
License Clarity
The model is under a 'Proprietary' license with no public text available for review. While it is currently 'free' for alpha testing on certain platforms, the long-term commercial terms, derivative works policy, and usage restrictions are not clearly defined in a standard legal framework accessible to the public.
Hardware Footprint
There is no documentation regarding VRAM requirements for local deployment, as the model is currently closed-weights and API-only. While it is marketed as 'optimized for NVIDIA hardware,' there are no public benchmarks showing memory scaling, quantization tradeoffs, or specific hardware requirements for inference.
Versioning Drift
The model uses the 'Alpha' designation, but there is no public changelog or semantic versioning system in place. Updates appear to be silent or 'stealth' in nature, with no documentation provided to users regarding behavioral changes or performance drift during the alpha period.