---
title: "State Space Models"
description: "2 posts about State Space Models, summarised, each linking to the original."
---

# State Space Models
> 2 posts about State Space Models, summarised, each linking to the original.

## Articles

### [Can we change an AI model’s architecture without retraining it from scratch?](https://yomu.fyi/post/can-we-change-an-ai-model-8217-s-architecture-without-retraining-it-fr.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Luca Zancato
- Published: Oct 12, 2026
- Type: Announcement

The Transformer architecture’s memory cost scales with token count, making long-context AI agents expensive. In controlled comparisons of three state space models - Gated KalmaNet, Gated DeltaNet, and Mamba-2 - Gated KalmaNet performed best, offering variable-length inference without retraining. Priming identifies which attention layers can be replaced by state space models using a sliding-window attention proxy, then reuses pre-trained weights to convert them into hybrid architectures without retraining, reducing pretraining cost by over 99%. A 32-billion-parameter hybrid model matches the original Transformer’s reasoning quality while decoding up to twice as fast or reaching correct answers in two-thirds of wall-clock time. The team extended hybrid context to 256K tokens without additional training and released primed models at 8B and 32B scales along with training code, kernels, and a vLLM plugin under Apache 2.0.


### [ReplaySSM: Rethinking State Storage for Faster Hybrid-Model Decoding](https://yomu.fyi/post/replayssm-rethinking-state-storage-for-faster-hybrid-model-decoding.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Oct 12, 2026
- Type: Problem & solution

Hybrid models interleave state-space model (SSM) layers with attention layers, but SSM layers can become a decoding bottleneck at serving batch sizes due to memory-bound state updates. ReplaySSM represents the SSM state as a checkpoint plus a buffer of recent inputs, separating output computation from state updates. This reduces state memory traffic by roughly half, simplifies rollback for speculative decoding by avoiding state snapshots, and supports low-precision state reads without propagating rounding error through the recurrence, enabling separate precision for output computation and state updates. Evaluated on Nemotron 3 and Qwen3.5 models from 4B to 120B parameters on H100 and B300 GPUs, ReplaySSM achieves 1.20× to 1.47× faster decode steps at batch size 256, with end-to-end gains growing with batch size. The technique is integrated into TensorRT-LLM, vLLM, SGLang, and FlashInfer, and runs in production in GLM-5.3-Flash and Kimi-K3.