OpenReasoning-Nemotron-32B
OpenReasoning-Nemotron-32B is a 32.7B parameter reasoning LLM built on Qwen2.5-32B, fine-tuned for math, code, and science problem-solving. It demonstrates strong performance on competitive benchmarks (AIME, MMLU-Pro, LiveCodeBench) and supports extended reasoning outputs up to 64K tokens. The model is ungated, openly available under CC-BY-4.0, and designed for developers building reasoning-heavy applications. It can operate in standard pass@1 mode or advanced "heavy" GenSelect mode for improved accuracy via ensemble-like solution selection.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | nvidia |
| Parameters | 32.8B |
| Context window | Unknown |
| License | cc-by-4.0 — Requires review (not clearly OSI) |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 67.3k |
| Likes | 126 |
| Last updated | 2025-09-16 |
| Source | nvidia/OpenReasoning-Nemotron-32B |
What OpenReasoning-Nemotron-32B is
32.7B-parameter transformer model derived from Qwen2.5-32B. Specialized for chain-of-thought reasoning across math, code, and science domains via supervised fine-tuning. Supports up to 64K output tokens. Available in multiple sizes (1.5B, 7B, 14B, 32B). Distributed via Hugging Face as safetensors, compatible with transformers pipeline and text-generation-inference. GenSelect capability enables combining multiple independent generations with learned solution selection. Training data (math, code, science) open-sourced separately. Last updated September 2025.
Run OpenReasoning-Nemotron-32B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="nvidia/OpenReasoning-Nemotron-32B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimate: 64–80 GB VRAM for bfloat16 inference (32.7B params × 2 bytes/param + overhead). A100 (80GB), H100 (80GB), or multi-GPU setup recommended. Quantization (8-bit/4-bit) feasibility Unknown; not documented in model card. GenSelect mode requires parallel GPU memory for multiple concurrent generations. CPU inference not practical.
Model card does not document LoRA/QLoRA support or fine-tuning guidelines. Base model (Qwen2.5-32B) is known to support adapter-based fine-tuning; compatibility expected but requires verification. Training data (Nemotron-Post-Training-Dataset-v1, OpenScienceReasoning-2) publicly available for reproducibility. No explicit guidance on instruction-tuning or catastrophic forgetting mitigation provided.
When to avoid it — and what to weigh
- Real-time, low-latency production systems — 32B parameter model requires significant VRAM (estimate 64GB+ for bfloat16) and inference time. GenSelect mode multiplies latency further via parallel generation. Not suitable for sub-second SLA requirements.
- General-purpose chat or instruction-following — Model is specialized for reasoning-heavy technical tasks. General conversational ability, creative writing, or broad knowledge QA are not optimized use cases. Designed for structured problem-solving, not open-ended dialogue.
- Deployment with strict hardware constraints — Requires modern GPU with substantial VRAM. Quantization (8-bit/4-bit) options not documented. Embedded, edge, or CPU-only deployments not feasible without separate optimization effort.
- Tasks requiring domain knowledge beyond reasoning — Model excels at logic and calculation but may lack current knowledge, proprietary domain facts, or personalization. Not a knowledge base replacement; RAG integration recommended for fact-heavy applications.
License & commercial use
Dual-licensed under CC-BY-4.0 (primary, Creative Commons Attribution 4.0 International) and Apache 2.0 (secondary, inherited from Qwen2.5-32B base model). CC-BY-4.0 is an open, attribution-required license permitting commercial and non-commercial use.
Model card explicitly states: "This model is ready for commercial/non-commercial research use." CC-BY-4.0 is OSI-compatible and permits commercial use provided attribution is given. No additional restrictions (gating, signature requirement) documented. Legal review recommended for production deployments to ensure compliance with attribution terms and Qwen2.5 Apache 2.0 base model obligations.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model specialized for reasoning; inherent injection/prompt-injection risks depend on application design. No dedicated security audit or adversarial robustness evaluation documented. Considerations: (1) Extended reasoning outputs (64K tokens) may amplify jailbreak payloads; (2) code generation may produce unsafe or insecure code without post-processing validation; (3) training on competitive problem datasets may memorize solutions; (4) no mention of differential privacy or PII scrubbing in training data. Recommend output validation, sandboxing for code execution, and application-layer guardrails.
Alternatives to consider
DeepSeek-R1 (70B or smaller variants)
Larger sibling achieving 93%+ on AIME with native chain-of-thought. Higher accuracy but ~2× model size; may require more VRAM. Closed training methodology vs. Nemotron's open data.
Qwen2.5-32B-Instruct (base model)
Smaller, lighter base without reasoning specialization. Better for general-purpose tasks; insufficient for competitive math/code benchmarks without fine-tuning. Simpler deployment.
Claude 3.5 Sonnet (commercial API)
Managed service eliminating deployment complexity and VRAM constraints. Superior general knowledge and safety. Higher per-token cost; no data ownership; not suitable for private-LLM or offline use cases.
Ship OpenReasoning-Nemotron-32B with senior software developers
OpenReasoning-Nemotron-32B is ideal for teams building specialized reasoning systems or automating technical problem-solving. Start with a proof-of-concept on a single A100 GPU, then evaluate GenSelect mode for production accuracy/latency trade-offs. Contact our team to architect a custom reasoning pipeline or compare with commercial alternatives.
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OpenReasoning-Nemotron-32B FAQ
Can I use this model commercially without paying royalties?
What GPU do I need to run this locally?
How does GenSelect mode improve accuracy?
Is this model suitable for general-purpose chat?
Software developers & web developers for hire
Need help beyond evaluating OpenReasoning-Nemotron-32B? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Reasoning Intelligence?
OpenReasoning-Nemotron-32B is ideal for teams building specialized reasoning systems or automating technical problem-solving. Start with a proof-of-concept on a single A100 GPU, then evaluate GenSelect mode for production accuracy/latency trade-offs. Contact our team to architect a custom reasoning pipeline or compare with commercial alternatives.