File size: 2,810 Bytes
d32801d f96a57c d32801d 2cbcf1b d32801d e70d37d d32801d f96a57c d32801d f96a57c d32801d 2cbcf1b e70d37d d32801d f96a57c d32801d f96a57c d32801d f96a57c d32801d e70d37d d32801d f96a57c 5502ba2 f96a57c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # NIM AI
**NIM AI** engineers compute-efficient, high-density reasoning engines, sparse architectures, and autonomous agent runtimes optimized for edge execution and accessible developer environments.
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### Mission
Frontier intelligence should not be confined to massive multi-node cloud clusters. NIM AI develops purpose-built models and distilled architectures that maximize token throughput per watt, architectural density, and verifiable reasoning.
* **Compute & Memory Efficiency:** Architected with aggressive parameter sparsity, low-precision scaling, and compact runtimes to run within consumer GPU and unified memory envelopes.
* **Deliberative Reasoning:** Engineered with structured chain-of-thought (`<think>`) traces to plan edge cases, algorithmic complexity, and concurrency boundaries before emitting code.
* **Native Edge Deployment:** Shipped as self-contained GGUF binaries (`Q8_0`, `Q4_K_M`) for single-command orchestration across Ollama, `llama.cpp`, and local IDE integrations.
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### Flagship Releases
| Model | Architecture | Size | Active Params | Target Use Case | Deployment Command |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **NIM-1** | Dense SLM | 3.09B | 3.09B | Low-latency triage, structured extraction, edge CLI automation | `ollama run hf.co/N-I-M-AI/NIM-1-3B:NIM-1-3B-Q8_0.gguf` |
| **NIM-2 Coder** | Dense Coder | 7.61B | 7.61B | Local code refactoring, unit test synthesis, fast inline completion | `ollama run hf.co/N-I-M-AI/NIM-2-Coder-7B:NIM-2-Coder-7B-Q4_K_M.gguf` |
| **NIM-3 Coder** | Sparse MoE | 30.5B | ~3.0B | Systems engineering, high-concurrency architecture, complex algorithmic design | `ollama run hf.co/N-I-M-AI/NIM-3-Coder-30B:Q4_K_M` |
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### Architecture & Training Methodology
* **Sparse Dynamic Routing:** Leveraging fine-grained Mixture-of-Experts (MoE) designs to provide the world knowledge and capacity of a 30B-scale model while executing token generation at the latency and cost of a 3B engine.
* **Reasoning Distillation:** Supervised fine-tuning across complex concurrency patterns, lock-free memory structures, distributed consensus, and verification-driven algorithmic datasets.
* **Efficient Quantization & Optimization:** Native ChatML formatting, memory-efficient LoRA/QLoRA adaptation, and direct-to-binary compilation via optimized `Q4_K_M` / `Q8_0` GGUF pipelines.
* **Ecosystem Compatibility:** Seamless drop-in support for Ollama, `llama.cpp`, LM Studio, and OpenAI-compatible API servers for VS Code (Continue / Cline).
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### Connect & Contribute
* **Hugging Face Hub:** [huggingface.co/NIM-AI](https://huggingface.co/NIM-AI)
* **GitHub Organization:** [github.com/N-I-M-AI](https://github.com/N-I-M-AI)
* **License:** Permissive open-source research and commercial deployment under the Apache 2.0 license.
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