gpt-oss-puzzle-88B is a deployment-optimized large language model developed by NVIDIA, derived from OpenAI's gpt-oss-120b.
The model is produced using Puzzle, a post-training neural architecture search (NAS) framework, with the goal of significantly improving inference efficiency for reasoning-heavy workloads while maintaining or improving accuracy across reasoning budgets.

The model is specifically optimized for long-context and short-context serving on NVIDIA H100-class hardware, where reasoning models are often bottlenecked by KV-cache bandwidth and memory capacity rather than raw compute.

Compared to its parent, gpt-oss-puzzle-88B:

• Reduces total parameters to ~88B (≈73% of the parent),
• Achieves 1.63× throughput improvement in long-context (64K/64K) scenarios on an 8×H100 node,
• Achieves 1.22× throughput improvement in short-context (4K/4K) scenarios,
• Delivers up to 2.82× throughput improvement on a single H100 GPU,
• Matches or slightly exceeds parent accuracy across reasoning efforts.

Model Architecture

• Architecture Type: Mixture-of-Experts Decoder-only Transformer
• Network Architecture: Modified gpt-oss architecture with varying number of experts per layer, and a modified global/window attention pattern across layers.
• Number of model parameters: 88B