amazon/GDN-primed-HQwen3-32B-Instruct

GDN-primed-HQwen3-32B-Instruct

GDN-primed-HQwen3-32B-Instruct is a Hybrid language model consisting of 50% Attention layers and 50% Gated DeltaNet (GDN) layers, primed from Qwen3-32B using the Hybrid Model Factory Priming pipeline. The model is instruction-tuned and supports context lengths up to 128K tokens.

GDN is a State-Space Model layer with constant memory and linear compute cost in the sequence length.

By combining Attention with GDN, our Hybrid model achieves up to 2× faster inference at long contexts while closely matching the base Transformer's quality.

Links

• 📄 Gated DeltaNet paper (ICLR 2025)
• 💻 GitHub: Hybrid Model Factory

Why Hybrid?

Each Primed Hybrid model is initialized from a base Transformer by converting a portion of its Attention layers into State-Space Model (SSM) layers that maintain a fixed-size recurrent state instead of a growing KV cache. At a 50% Hybrid ratio, roughly half the KV cache (which grows linearly with sequence length) is replaced with fixed-size SSM state. The practical benefits:

• Higher throughput at long contexts — less memory on KV cache means more memory for batching
• More concurrent sequences — ~2× as many concurrent sequences before hitting memory limits
• Growing advantage with context length — at long contexts, Attention dominates the forward pass while SSM layers remain negligible in cost. Since the Hybrid model makes roughly half as many Attention calls as the base Transformer, the throughput advantage grows with context length

Increasing hybridization ratio, replacing more Attention layers with SSM layers, further reduces memory and increases throughput, typically at the expense of performance.

Model Overview

• Type: Causal Language Model (Hybrid Attention + SSM)
• Base Model: Qwen3-32B
• Hybrid Layer Type: Gated DeltaNet (GDN)
• Hybrid Ratio: 50% (32 Attention + 32 GDN layers)
• Parameters: ~32B
• Context Length: 128K natively
• Precision: bfloat16
• License: Apache 2.0

Note, this is an Instruct-tuned model and is not a thinking model, that is, it does not natively produce chain-of-thought thinking tokens in its generation trace.

Benchmark Results

Below we report benchmark performance for all our instruct-tuned Primed models. All Hybrid models use a 50% Hybrid ratio and are Primed from Qwen3-32B.

We consider the following Transformer as a baseline:

• Qwen3-32B (Long): The Qwen model fine-tuned on our priming data, extending its native context length from 32K to 128K. All Primed Hybrid models use the same training hyperparameters and data as this baseline, making it a fair comparison for differing architectures.

On both long- and short-context benchmarks, our Primed Hybrid models closely match the performance of the Transformer model while having [considerably lower deployment costs](#inference-efficiency), showcasing the efficacy of the Priming process.

Long-Context Benchmarks

Evaluated on HELMET, MRCR, and BABILong across context lengths from 8K to 128K, using a weighted average with geometrically increasing weights for longer contexts.

The plot below shows performance averaged over context lengths from 8K to 128K.

How close are the Hybrid models to the Transformer baseline on long context tasks? Primed GKA and GDN hybrids have competitive long-context capabilities with a gap of ~2.5-3 points on average with the Transformer [Qwen3-32B (Long)], while being [1.5–2× faster at inference](#inference-efficiency) on long contexts.

Short-Context NLP Benchmarks

Evaluations on Tulu3-dev from OLMES. All tasks are over a short-context length (≤ 8K). Each category in the table below averages the following Tulu3-dev subtasks: 1. Math: GSM8K, MATH. 2. Knowledge: MMLU, PopQA, TruthfulQA. 3. Coding: HumanEval, HumanEval+. 4. Reasoning: BigBenchHard. 5. Instruction Following: IFEval.

| Model | Math | Knowledge | Coding | Reasoning | Instruction Following | Average | |------------------------------------|-------|----------|--------|-----------|-----------------------|---------| | Qwen3-32B [Long] | 74.43 | 54.47 | 94.54 | 82.89 | 81.52 | 77.56 | | GKA-primed-HQwen3-32B-Instruct | 74.02 | 53.95 | 93.43 | 80.31 | 78.74 | 76.09 | | GDN-primed-HQwen3-32B-Instruct | 73.65 | 54.35 | 94.40 | 80.99 | 79.3 | 76.54 |

How close are the Hybrid models to the Transformer baseline on short context tasks? Our Primed Hybrid models are within ~1-1.5 points of the average performance of the Transformer [Qwen3-32B (Long)] using [ [!NOTE] > For applications to complex reasoning and coding problems check out our Primed Hybrid Reasoning models.

About Gated DeltaNet (GDN)

Gated DeltaNet is a State-Space Model layer with diagonal + low-rank transition dynamics. It extends Mamba2 with the Delta Update rule, improving expressiveness through gated state transitions while retaining Mamba2's efficiency.

For more details, see the GDN paper.

Architecture Details

| Component | Details | |-----------|------------------------------------------------------------------------------| | Number of Layers | 64 (32 Attention + 32 GDN) | | Hidden Dimension | 5120 | | Attention Heads | 64 (Q) / 8 (KV) | | Head Dimension | 128 | | Intermediate Dimension (FFN) | 25600 | | Vocabulary Size | 151,936 | | Position Encoding | RoPE (θ = 5,000,000) | | Layer Layout | GDN layer indices were selected with our *selective hybridization* procedure |

Inference Efficiency

Sustained decode throughput (tokens/s) on 8× H200 GPUs (TP=8), measured during pure decode with a saturated KV cache. Benchmarked with random data (no prefix-caching benefits). See the full Inference guide for methodology and additional models.

| Model | 16K | 32K | 64K | 128K |…