NIM-AI/NIM-3-Coder-30B

NIM 3 - Coder - 30B

Developed by NIM-AI
A Sparse Mixture-of-Experts (MoE) Reasoning Engine for Production Software Engineering

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Overview

NIM 3 - Coder - 30B is NIM-AI's high-efficiency coding and reasoning model, packaged in optimized GGUF format for fast local execution. Built on a Sparse Mixture-of-Experts (MoE) architecture, the model routes token predictions dynamically through dedicated expert networks, activating approximately 3 billion parameters per token out of its total 30.5-billion-parameter pool.

This provides the architectural reasoning and comprehension of a 30B-class model while preserving the execution speed, low latency, and memory footprint of a lightweight system.

NIM 3 - Coder is trained with an explicit Chain-of-Thought (CoT) objective. Prior to synthesizing code, the engine outputs deliberative <think>...</think> traces to systematically evaluate:

• Concurrency patterns, async runtimes, and thread safety
• Boundary conditions, resource allocation, and edge cases
• Algorithmic complexity and execution bottlenecks
• Clean, idiomatic implementation patterns across production stacks

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Available Files

Filename	Quantization	Size	Description
Qwen3-30B-A3B.Q4_K_M.gguf	Q4_K_M	~18.6 GB	Balanced 4-bit medium quantization. Recommended balance of reasoning fidelity and speed.

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Technical Specifications

Parameter	Specification
Total Parameters	30.5 Billion
Active Parameters / Token	~3.0 Billion (Sparse Dynamic Routing)
Format	GGUF (Ready for Ollama, llama.cpp, and LM Studio)
Context Window	4,096 tokens (Native, scalable via RoPE)
Primary Languages	Rust, Python, Go, TypeScript/JavaScript, C++, SQL, Bash
Specialization	Systems Architecture, Concurrency, Algorithmic Optimization, Backend Pipelines

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Quickstart Guide

1. Run Instantly with Ollama

You can pull and launch NIM 3 - Coder directly using Ollama without manual file downloads:

ollama run hf.co/NIM-AI/NIM-3-Coder-30B:Q4_K_M

To create a custom local alias in Ollama, create a file named Modelfile:
FROM ./Qwen3-30B-A3B.Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.6

Then build and launch:
ollama create nim-3-coder -f Modelfile
ollama run nim-3-coder

Run an OpenAI-compatible API server on your local machine or cloud GPU:
./llama-server \
    -m Qwen3-30B-A3B.Q4_K_M.gguf \
    --port 8000 \
    --ctx-size 4096 \
    --n-gpu-layers 99 \
    --host 0.0.0.0

Desktop Inference via LM Studio
Open LM Studio.
Search for NIM-AI/NIM-3-Coder-30B in the search bar.
Download the Q4_K_M file and load it into the local chat interface.

Prompt Template
NIM 3 - Coder utilizes the standard ChatML format. Maintain the system prompt to trigger reasoning blocks:
<|im_start|>system
You are NIM 3 - Coder, a world-class software engineer. Always think step-by-step inside <think>...</think> tags before writing clean, high-performance code.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
{reasoning}
</think>

{solution}
<|im_end|>

Usage Guidelines & Notes
Thinking Output: The model generates reasoning traces inside <think>...</think>. If integrating with an IDE agent or terminal pipe, ensure your interface supports streaming or extracts the content past </think>.
Hardware Sizing: Because the full 30.5B expert weights reside in memory, running the Q4_K_M binary requires ~19 GB of memory headroom (GPU VRAM or shared unified memory on Apple Silicon).
Organization & Contact
Engineered and maintained by NIM-AI. For enterprise configurations, updates, and ecosystem tooling, visit the NIM-AI Organization Hub.