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\title{Mamba in the Browser: First Browser-Native Selective State Space Model Inference via Hand-Written WebGPU Compute Shaders}

\author{
  Joshua Michael\thanks{Independent researcher. Correspondence: artfullofjoy@gmail.com} \\
  \textit{with computational collaboration from Claude (Anthropic)}
}

\date{May 2026}

\begin{document}
\maketitle

\begin{abstract}
State space models (SSMs) with selective scan mechanisms, particularly Mamba, offer fixed-size persistent state as an alternative to the growing key-value caches of transformer architectures. However, no existing browser-based inference framework supports pure Mamba models: MLC/WebLLM lacks SSM support entirely, ONNX export of the selective scan operator remains an unsolved problem, and existing browser SSM runtimes target architecturally distinct models (RWKV). We present, to our knowledge as of May 2026, the first browser-native inference engine for pure Mamba models, implemented as 12 hand-written WGSL compute shaders running on the WebGPU API. Our runtime loads Falcon-Mamba-7B-Instruct (14GB, F32) directly from safetensors via byte-range fetch, executes the full 64-layer forward pass including the selective state update, and generates coherent text at approximately 3 tokens/second on an AMD RDNA-3 integrated GPU. All shader operations are validated against PyTorch to $\leq 10^{-6}$ relative error across 8,192-element buffers. The 38MB SSM state is persistent and serializable, enabling stateful entities that maintain continuity across sessions without server infrastructure. Code and shaders are publicly available at \url{https://huggingface.co/LJTSG/mamba-webgpu}.
\end{abstract}

\section{Introduction}

Browser-native neural network inference eliminates server dependencies, reduces latency, and enables privacy-preserving on-device computation. WebLLM~\cite{webllm} and MLC-LLM~\cite{mlc} have demonstrated that transformer models can run efficiently in the browser via the WebGPU API, using the TVM compiler to generate optimized GPU kernels. However, this compiler-driven approach is architecture-specific: as of May 2026, MLC-LLM does not support the Mamba~\cite{mamba} selective state space architecture, and an open feature request (GitHub issue \#2348) has no implementation or timeline.

The obstacle is fundamental, not incidental. Mamba's selective scan operator uses input-dependent gating---the state transition matrices $\mathbf{B}$, $\mathbf{C}$, and the discretization step $\Delta$ are all functions of the input at each timestep, making the recurrence non-time-invariant. The reference implementation relies on a fused Triton/CUDA kernel for this operation~\cite{mamba}. Exporting this operator to ONNX is an open problem (PyTorch issue \#146835; state-spaces/mamba issue \#200), which blocks the entire ONNX Runtime WebGPU pathway used by Transformers.js~\cite{transformersjs}.

This creates a gap: state space models cannot reach the browser through any existing compilation or export pipeline. Yet SSMs have a property uniquely suited to browser deployment: \textit{fixed-size persistent state}. Unlike transformers, whose KV cache grows linearly with context, a Mamba model's entire recurrent state is a fixed 38MB (for a 7B model with 64 layers, hidden dimension 8192, and state size 16). This state can be saved to disk and restored, enabling persistent entities that maintain continuity across sessions---without a server.

We bridge this gap by implementing the full Mamba forward pass as 12 hand-written WGSL compute shaders, bypassing the compilation toolchain entirely. Our runtime loads Falcon-Mamba-7B-Instruct~\cite{falconmamba} weights directly from safetensors format, performs BF16$\to$F32 conversion on the CPU, and executes autoregressive generation through 64 layers of the selective state space architecture. We validate every intermediate computation against PyTorch with element-wise golden-value comparison, achieving $\leq 10^{-6}$ relative error across all 8,192 channels.

\section{Related Work}

\paragraph{WebLLM and MLC-LLM.} The MLC compiler~\cite{mlc} generates optimized WebGPU kernels for transformer architectures and powers the WebLLM~\cite{webllm} project. It supports attention-based models including Llama, Mistral, and Phi but does not support Mamba or other SSM architectures.

\paragraph{web-rwkv.} The web-rwkv project~\cite{webrwkv} implements RWKV inference in the browser using wgpu (Rust) compiled to WASM with WebGPU backend. RWKV is an SSM-adjacent architecture using linear attention and channel-mixing, but its recurrence is structurally simpler than Mamba's selective scan: RWKV's state transition parameters are \textit{fixed} (learned during training), while Mamba's are \textit{input-dependent} (computed from each token). This architectural difference is precisely what makes Mamba harder to implement and what prevents existing compilation pipelines from supporting it. We cite web-rwkv as important related work demonstrating the viability of browser-native recurrent inference.

\paragraph{Transformers.js and ONNX Runtime.} Transformers.js v4~\cite{transformersjs} claims Mamba support, but its model list includes only hybrid architectures (Falcon-H1, Nemotron-H) that combine Mamba layers with attention layers. Pure Mamba models cannot be exported to ONNX because the selective scan operator has no registered ONNX exporter.

\paragraph{Hand-written WGSL inference.} bitnet.js implements a transformer inference engine using hand-crafted WGSL shaders in the browser. wgpu-llm does the same in Rust (native only). Neither implements SSM/Mamba operations.

\section{Background: The Selective Scan}

The core operation in Mamba is the \textit{selective state update} (SSU). Given input $\mathbf{x} \in \mathbb{R}^{D}$ at a single timestep, the SSU computes:

\begin{align}
\boldsymbol{\delta} &= \text{softplus}(\mathbf{W}_\Delta \mathbf{x} + \mathbf{b}_\Delta) \\
\bar{\mathbf{A}} &= \exp(\boldsymbol{\delta} \odot \mathbf{A}) \\
\bar{\mathbf{B}} &= \boldsymbol{\delta} \odot \mathbf{B}(\mathbf{x}) \\
\mathbf{h}_t &= \bar{\mathbf{A}} \odot \mathbf{h}_{t-1} + \bar{\mathbf{B}} \odot \mathbf{x} \\
\mathbf{y}_t &= \mathbf{C}(\mathbf{x})^\top \mathbf{h}_t + \mathbf{D} \odot \mathbf{x}
\end{align}

where $\mathbf{A} \in \mathbb{R}^{D \times N}$ is the (fixed, negative) state decay matrix, $\mathbf{B}(\mathbf{x}), \mathbf{C}(\mathbf{x}) \in \mathbb{R}^{N}$ are \textit{input-dependent} projection matrices, $\boldsymbol{\delta} \in \mathbb{R}^{D}$ is the input-dependent discretization step, and $\mathbf{h}_t \in \mathbb{R}^{D \times N}$ is the persistent state. For Falcon-Mamba-7B, $D = 8192$ and $N = 16$, giving a per-layer state of 512KB.

The input-dependent parametrization of $\mathbf{B}$, $\mathbf{C}$, and $\boldsymbol{\delta}$ is what makes Mamba selective---and what makes it resistant to compilation. The fused CUDA/Triton kernel in the reference implementation handles this entire operation in one launch. Our WGSL implementation decomposes it into separate compute passes (x\_proj, dt\_proj, SSU) with explicit buffer management.

\paragraph{Falcon-Mamba architectural modification.} Falcon-Mamba~\cite{falconmamba} adds a step not present in the original Mamba architecture: weightless RMS normalization of $\mathbf{B}$, $\mathbf{C}$, and the pre-projection $\boldsymbol{\delta}_\text{pre}$ \textit{before} they enter the SSU. This is implemented as:
\begin{equation}
\hat{\mathbf{v}} = \frac{\mathbf{v}}{\sqrt{\text{mean}(\mathbf{v}^2) + \epsilon}}
\end{equation}
applied independently to each of $\mathbf{B}$, $\mathbf{C}$, and $\boldsymbol{\delta}_\text{pre}$. This normalization is not documented in the Falcon-Mamba model card or architecture description---it is visible only in the HuggingFace source code (\texttt{FalconMambaMixer.slow\_forward}). Discovering this missing step required comparing our runtime's output against the actual model forward pass rather than against our own reference implementation (Section~\ref{sec:validation}).

\section{Method}

\subsection{Shader Architecture}

We implement 12 WGSL compute shaders covering all operations in the Falcon-Mamba forward pass. Table~\ref{tab:shaders} lists each shader with its function and dispatch configuration.

\begin{table}[h]
\centering
\small
\begin{tabular}{@{}lll@{}}
\toprule
\textbf{Shader} & \textbf{Operation} & \textbf{WG} \\
\midrule
\texttt{rmsnorm} & RMSNorm with weights & 64 \\
\texttt{rmsnorm\_noweight} & Weightless RMSNorm (B/C/$\delta$) & 64 \\
\texttt{matmul\_gemv} & Matrix-vector product & 64 \\
\texttt{conv1d\_step} & Depthwise conv1d with state & 64 \\
\texttt{ssu} & Selective state update & 16 \\
\texttt{silu} & SiLU activation (in-place) & 64 \\
\texttt{elementwise\_mul} & Element-wise multiply & 64 \\
\texttt{add\_residual} & Residual add (in-place) & 64 \\
\texttt{sample} & Temperature sampling & 256 \\
\texttt{softplus} & Softplus activation & 64 \\
\texttt{embedding} & Embedding lookup & -- \\
\texttt{bf16\_to\_f32} & BF16$\to$F32 conversion & 64 \\
\bottomrule
\end{tabular}
\caption{The 12 WGSL compute shaders. WG = workgroup size.}
\label{tab:shaders}
\end{table}

Each layer of the forward pass dispatches approximately 15 shader invocations: RMSNorm, in\_proj GEMV, conv1d\_step, SiLU, x\_proj GEMV, three weightless RMSNorms (on B, C, $\delta_\text{pre}$), dt\_proj GEMV, SSU, SiLU (gate), elementwise multiply, out\_proj GEMV, and residual add. For 64 layers plus the final RMSNorm, lm\_head, and sampling, each token requires approximately 975 shader dispatches.

\subsection{Weight Loading}

Weights are loaded from HuggingFace safetensors format via HTTP byte-range requests. The safetensors header (JSON, typically 20--26KB) is fetched first to obtain tensor metadata (shape, dtype, byte offsets). Each tensor is then fetched individually using Range headers, converted from BF16 to F32 on the CPU via bit-shift ($\text{bf16} \ll 16$), and uploaded to a WebGPU storage buffer. The full 643-tensor, 14GB model loads in approximately 60 seconds.

\subsection{State Management}

Each of the 64 layers maintains two persistent GPU buffers: the SSM state ($D \times N = 8192 \times 16$ floats, 512KB) and the conv1d state ($D \times (K-1) = 8192 \times 3$ floats, 96KB). Total persistent state: $64 \times 608\text{KB} \approx 38\text{MB}$. The runtime exposes \texttt{saveState()} and \texttt{restoreState()} methods that read/write these buffers to CPU memory, enabling serialization to IndexedDB or file.

\subsection{WebGPU Constraints}

Several WebGPU-specific constraints required adaptation:

\begin{itemize}
\item \textbf{Storage buffer offset alignment.} WebGPU requires storage buffer binding offsets to be multiples of \texttt{minStorageBufferOffsetAlignment} (typically 256 bytes). Extracting sub-tensors (B, C from the x\_proj output) with non-aligned offsets silently invalidates the entire command encoder. We resolved this by copying sub-tensors into separate aligned buffers.

\item \textbf{Storage buffer count limit.} The SSU shader requires 9 storage buffer bindings. The default \texttt{maxStorageBuffersPerShaderStage} is 8. We request the adapter's supported maximum (16 on our hardware).

\item \textbf{Buffer usage restrictions.} \texttt{MAP\_READ} cannot be combined with \texttt{STORAGE} usage. Token readback uses a separate staging buffer.
\end{itemize}

\section{Validation}
\label{sec:validation}

We validate the runtime by comparing every intermediate buffer against PyTorch's computation of the same forward pass, using the same F32 weights and the same input token.

\subsection{Per-Operation Verification}

For each operation in layer 0, we read back the GPU output buffer and compare element-wise against PyTorch:

\begin{table}[h]
\centering
\small
\begin{tabular}{@{}lcc@{}}
\toprule
\textbf{Operation} & \textbf{Max |err|} & \textbf{Elements} \\
\midrule
Embedding & $< 10^{-6}$ & 4,096 \\
RMSNorm & $< 10^{-6}$ & 4,096 \\
in\_proj GEMV & $< 10^{-6}$ & 16,384 \\
conv1d + SiLU & $< 10^{-6}$ & 8,192 \\
SSU (y output) & $< 10^{-6}$ & 8,192 \\
gate $\times$ SiLU & $< 10^{-6}$ & 8,192 \\
\bottomrule
\end{tabular}
\caption{Per-operation validation against PyTorch (layer 0, token ID 8).}
\label{tab:validation}
\end{table}

\subsection{Distrusting the Oracle}

Initial validation compared our WebGPU output against a manually-written PyTorch reference computation (\texttt{golden\_dump.py}). All operations matched to six decimal places, yet the full 64-layer output diverged from the actual model. Investigation revealed that our reference computation and our WebGPU runtime shared the same false assumption: both omitted the Falcon-Mamba-specific weightless RMSNorm on B, C, and $\delta_\text{pre}$.

The resolution required comparing against the \textit{actual model forward pass} (\texttt{model(input\_ids)}) rather than our manual reconstruction, then reading the HuggingFace model source (\texttt{FalconMambaMixer.slow\_forward}) to identify the three missing normalization calls. This class of bug---where the test and the code share the same incorrect specification---is undetectable by unit testing alone.

\section{Results}

\subsection{Performance}

Tested on AMD Strix Halo (Radeon 8060S iGPU, RDNA-3 architecture, 64GB unified system memory):

\begin{itemize}
\item \textbf{Generation speed:} $\sim$180ms/token ($\sim$3 tokens/second)
\item \textbf{Weight loading:} $\sim$60 seconds (14GB F32 via byte-range fetch)
\item \textbf{Persistent state:} 38MB (fixed, independent of context length)
\item \textbf{Dispatches per token:} $\sim$975 compute shader invocations
\end{itemize}

\subsection{Output Quality}

With the Falcon-Mamba instruct chat template applied, the runtime generates coherent, contextually appropriate multi-sentence responses. Greedy-decoded output matches PyTorch's \texttt{model.generate()} with cache for the same prompt and token sequence.

\section{Discussion}

The fixed-size persistent state of SSMs has implications beyond inference efficiency. A 38MB state file fully characterizes the model's ``memory'' of all prior context. Unlike transformer KV caches, this state does not grow with conversation length and can be serialized, stored, and restored across browser sessions. This enables a class of applications where a model-backed entity persists in the user's browser without any server infrastructure---the computational substrate for persistent, sovereign, local AI.

The hand-written shader approach, while labor-intensive, provides architectural flexibility that compilation pipelines cannot: we implemented the Falcon-Mamba-specific RMSNorm modification without waiting for upstream compiler support. As SSM architectures continue to evolve (Mamba-2, hybrid models, state-space variants), hand-written runtimes can adapt faster than compilation toolchains.

\section{Limitations}

\begin{itemize}
\item \textbf{F32 only.} No weight quantization; the full 14GB model must fit in GPU-accessible memory. Quantized formats (INT4/INT8) would reduce this to 3.5--7GB.
\item \textbf{Single-token decode.} No batch or prefill optimization. Each token processes through all 64 layers sequentially.
\item \textbf{Server-side tokenization.} The tokenizer runs via a Python subprocess on a local Node.js server. A browser-native tokenizer (e.g., Transformers.js) would eliminate this dependency.
\item \textbf{Hardware tested.} Results reported on one GPU (AMD RDNA-3 iGPU). Other vendors/architectures may require limit adjustments.
\item \textbf{Device loss.} WebGPU devices may be reclaimed by the browser when tabs lose focus, requiring re-initialization.
\end{itemize}

\section{Conclusion}

We present, to our knowledge as of May 2026, the first browser-native inference engine for the Mamba selective state space architecture, implemented as 12 hand-written WGSL compute shaders on the WebGPU API. Every operation is validated against PyTorch to $\leq 10^{-6}$ precision. The runtime generates coherent text from Falcon-Mamba-7B-Instruct at 3 tokens/second with 38MB of persistent, serializable state. Code and shaders are available at \url{https://huggingface.co/LJTSG/mamba-webgpu}.

\begin{thebibliography}{9}

\bibitem{mamba}
A.~Gu and T.~Dao, ``Mamba: Linear-Time Sequence Modeling with Selective State Spaces,'' \textit{arXiv:2312.00752}, 2023.

\bibitem{falconmamba}
TII, ``Falcon Mamba: The First Competitive Attention-Free 7B Language Model,'' \textit{Technical Report}, 2024.

\bibitem{webllm}
C.~Chen et al., ``WebLLM: A High-Performance In-Browser LLM Inference Engine,'' \textit{MLSys}, 2024.

\bibitem{mlc}
T.~Chen et al., ``MLC-LLM: Universal LLM Deployment Engine with ML Compilation,'' 2023.

\bibitem{transformersjs}
X.~Hugging Face, ``Transformers.js v4,'' 2025. \url{https://huggingface.co/docs/transformers.js}

\bibitem{webrwkv}
cryscan, ``web-rwkv: An Implementation of the RWKV Language Model in Pure WebGPU,'' 2024. \url{https://github.com/cryscan/web-rwkv}

\end{thebibliography}

\end{document}
