NVIDIA researchers have shattered the longstanding efficiency hurdle in large language model (LLM) inference, releasing Jet-Nemotron—a family of models (2B and 4B) that delivers up to 53.6× higher generation throughput than leading full-attention LLMs while matching, or even surpassing, their accuracy. Most importantly, this breakthrough isn’t the result of a new pre-training run from scratch, but rather a retrofit of existing, pre-trained models using a novel technique called Post Neural Architecture Search (PostNAS). The implications are transformative for businesses, practitioners, and researchers alike.
The Need for Speed in Modern LLMs
While today’s state-of-the-art (SOTA) LLMs, like Qwen3, Llama3.2, and Gemma3, have set new benchmarks for accuracy and flexibility, their O(n²) self-attention mechanism incurs exorbitant costs—both in compute and memory—especially for long-context tasks. This makes them expensive to deploy at scale and nearly impossible to run on edge or memory-constrained devices. Efforts to replace full-attention Transformers with more efficient architectures (Mamba2, GLA, RWKV, etc.) have struggled to close the accuracy gap, until now.
PostNAS: A Surgical, Capital-Efficient Overhaul
The core innovation is PostNAS: a neural architecture search pipeline designed specifically for efficiently retrofitting pre-trained models. Here’s how it works:
- Freeze the Knowledge: Start with a SOTA full-attention model (like Qwen2.5). Freeze its MLP layers—this preserves the model’s learned intelligence and greatly reduces training cost.Surgical Replacement: Replace computationally expensive full-attention (Transformers) with JetBlock, a new, hardware-efficient linear attention block designed for NVIDIA’s latest GPUs.Hybrid, Hardware-Aware Design: Use super-network training and beam search to automatically determine the optimal placement and minimal set of full-attention layers necessary to preserve accuracy on key tasks (retrieval, math, MMLU, coding, etc.). This step is task-specific and hardware-aware: the search maximizes throughput for target hardware, not just parameter count.Scale and Deploy: The result is a hybrid-architecture LLM that inherits the backbone intelligence of the original model but slashes latency and memory footprint.
JetBlock is particularly noteworthy: it introduces dynamic causal convolution kernels conditioned on input (unlike static kernels in prior linear attention blocks) and removes redundant convolutions for streamlined efficiency. With hardware-aware hyperparameter search, it not only keeps pace with prior linear attention designs in throughput, but actually boosts accuracy.
Jet-Nemotron: Performance by the Numbers
The key metrics from NVIDIA’s technical paper are staggering:
| Model | MMLU-Pro Acc. | Generation Throughput (tokens/s, H100) | KV Cache Size (MB, 64K context) | Notes |
|---|---|---|---|---|
| Qwen3-1.7B-Base | 37.8 | 61 | 7,168 | Full-attention baseline |
| Jet-Nemotron-2B | 39.0 | 2,885 | 154 | 47× throughput, 47× smaller cache |
| Jet-Nemotron-4B | 44.2 | 1,271 | 258 | 21× throughput, still SOTA acc. |
| Mamba2-2.7B | 8.6 | 2,507 | 80 | All-linear, much lower accuracy |
| RWKV7-1.5B | 13.4 | 3,050 | 24 | All-linear, much lower accuracy |
| DeepSeek-V3-Small (MoE) | — | — | — | 2.2B activated, 15B total, lower acc. |
Jet-Nemotron-2B matches or exceeds Qwen3-1.7B-Base on every major benchmark—math, commonsense, coding, retrieval, long-context—while delivering 47× higher generation throughput.
This isn’t a small gain: a 53.6× speedup in decoding at 256K context length means a 98% reduction in inference cost for the same volume of tokens. Prefilling speedups are also dramatic: 6.14× faster at 256K context.
Memory footprint shrinks by 47× (154MB cache vs. 7,168MB for Qwen3-1.7B-Base). This is a game-changer for edge deployment: Jet-Nemotron-2B is 8.84× and 6.5× faster than Qwen2.5-1.5B on Jetson Orin and RTX 3090, respectively.
Applications
For Business Leaders: Better ROI $$
- Inference at scale is now affordable. A 53× throughput gain means dollar-for-dollar, you can serve 53× more users—or slash hosting costs by 98%.Operational efficiency is transformed: latency drops, batch sizes grow, and memory constraints vanish. Cloud providers can offer SOTA AI at commodity prices.The AI business model reshapes: Tasks once too expensive (real-time document AI, long-context agents, on-device copilots) suddenly become viable.
For Practitioners: SOTA on the Edge
- Forget about quantization, distillation, or pruning compromises. Jet-Nemotron’s tiny KV cache (154MB) and 2B parameters fit on Jetson Orin, RTX 3090, and even mobile chips—no more offloading to the cloud.No retraining, no data pipeline changes: Just retrofitting. Your existing Qwen, Llama, or Gemma checkpoints can be upgraded without losing accuracy.Real-world AI services (search, copilots, summarization, coding) are now instant and scalable.
For Researchers: Lower Barrier, Higher Innovation
- PostNAS slashes the cost of LLM architecture innovation. Instead of months and millions on pre-training, architecture search happens on frozen backbone models in a fraction of the time.Hardware-aware NAS is the future: The Jet-Nemotron process considers KV cache size (not just parameters) as the critical factor for real-world speed. This is a paradigm shift in how we measure and optimize efficiency.The community can iterate faster: PostNAS is a rapid testbed. If a new attention block works here, it’s worth pre-training; if not, it’s filtered out before the big spend.
Summary
The open-sourcing of Jet-Nemotron and JetBlock (code on GitHub) means the broader AI ecosystem can now retrofit their models for unprecedented efficiency. PostNAS is not a one-off trick: it’s a general-purpose framework for accelerating any Transformer, lowering the cost of future breakthroughs.
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