NVIDIA
OpenReasoning Nemotron 14B
Mid-sized dense Nemotron checkpoint for users who want stronger reasoning behavior than 7B without stepping straight into 32B deployment territory.
Overview and architecture
What it is
Company
Family
Release date
Architecture
License
Modality
Context window
Total params
Active params
Layers
Hidden size
Attention heads
KV heads
KV-bearing layers
Research highlight
What improved
Reasoning-first post-training
NVIDIA positions the 14B Nemotron model around stronger math, code, and science reasoning rather than around a new base architecture.
Qwen2.5-derived backbone
The family stays close to a Qwen2.5 dense grouped-query backbone, so the main change is in post-training behavior and benchmark profile, not in memory geometry.
GenSelect heavy mode
The model card explicitly introduces a heavier multi-sample inference path through GenSelect, which matters because capability can scale at inference time without changing the resident model itself.
Benchmark-led release framing
NVIDIA markets the line primarily through reasoning benchmark results in its size class, so this is a capability-tuned release more than an architecture-tuned one.
Training and release context
How it was released
Base-model inheritance
OpenReasoning-Nemotron models are NVIDIA post-training releases built directly on top of Qwen2.5 dense backbones.
Release method
The family is released as a reasoning-tuned derivative line rather than as a new architecture family with different serving mechanics.
Optional heavy mode
NVIDIA pairs the base checkpoints with GenSelect-style multi-sample inference guidance, so part of the release story lives in inference strategy rather than in the resident model alone.
Where it is strong
Where it is strong
Math and science reasoning
NVIDIA positions the family around benchmark-heavy reasoning workloads.
Code generation
The release emphasizes code and solution-generation performance alongside math.
Test-time scaling
GenSelect gives the family a clear path to higher-quality heavy inference when latency is less constrained.
Memory behavior
What dominates VRAM
This is still a dense 14B-class checkpoint: weights dominate the fit decision, and context length becomes the next major lever after quantization.
Sources