Alpie Core
AIA 32B, 4-bit quantized reasoning model optimized for multi-step reasoning and efficient deployment.
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Starting from Free
Premium plans available
Alpie Core
AI
A 32B, 4-bit quantized reasoning model optimized for multi-step reasoning and efficient deployment.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Alpie Core
Alpie Core is a 32-billion-parameter reasoning model developed by 169Pi that is trained, fine-tuned, and served entirely at 4-bit precision. The model focuses on strong multi-step reasoning and coding capabilities while keeping memory and inference costs low, enabling inference on much lower VRAM hardware than comparable large models. Alpie Core is positioned as an open-source milestone (hosted on Hugging Face) and is accessible through 169Pi's API platform, playground, SDKs, and developer documentation for building agents, apps, and research experiments.
Screenshots
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Key Features
4-bit Quantization: Trained, fine-tuned, and served entirely at 4-bit precision to significantly reduce VRAM and memory requirements during inference while preserving strong performance.
Large-scale Reasoning (32B): A 32-billion-parameter architecture optimized for multi-step reasoning tasks and complex chain-of-thought style problems.
Coding and Multi-step Problem Solving: Demonstrates strong performance on coding and multi-step reasoning benchmarks, making it suited for program synthesis and logical task workflows.
Low-VRAM Inference: Designed to run on consumer or modest GPU setups due to aggressive quantization, enabling broader accessibility without supercomputer-class hardware.
API & Platform Access: Available through 169Pi's API platform and global playground with SDKs and developer documentation for building agents and applications.
Open-Source Availability: Model weights and artifacts are published on Hugging Face, enabling researchers and developers to inspect, fine-tune, and deploy locally.
Benchmark-validated Performance: Public benchmark results (e.g., SWE-Bench) demonstrate competitive accuracy relative to larger or non-quantized models.
32B-parameter model architecture optimized for reasoning
End-to-end 4-bit quantization (trained, fine-tuned, and served at 4-bit)
Strong multi-step reasoning and coding capabilities
Low VRAM inference — designed to run without supercomputer-class hardware
Available via 169Pi API platform with global playground
SDKs and developer documentation for integration
Model card and weights published on Hugging Face
Fine-tuned for downstream performance and benchmarked (e.g., SWE-Bench)
Use Cases
Deploying reasoning-heavy applications: Integrate Alpie Core into systems that require multi-step logical reasoning such as decision-support agents, QA pipelines, and chain-of-thought workflows.
Code generation and assistance: Use the model for code completion, synthesis, and program repair where multi-step reasoning over code structure is required.
Edge or cost-constrained inference: Run advanced language-model workloads on lower-VRAM GPUs or on-premise servers thanks to 4-bit quantization.
Research into quantized LLMs: Benchmarking and experimenting with 4-bit training/serving techniques and open research into efficient large-model design.
Building conversational agents and assistants: Power assistants and chatbots that need reliable multi-step reasoning combined with efficient inference costs.
Embedded product prototypes: Rapidly prototype products that need large-model capabilities without cloud-only dependencies by using local or hybrid deployment models.
Multi-step reasoning tasks and complex chain-of-thought workflows
Code generation, debugging, and programming assistance
Research and benchmarking on quantized large models
Embedding into agents, apps, and services via API/SDK
Deployments where low VRAM inference is required (edge or constrained servers)
