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Alain Airom (Ayrom)
Alain Airom (Ayrom)

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Mamba vs. Transformers: Architecture Comparison

#mamba #transformers #llm #granite

My latest studies and understandings of Mamba versus Transformers!

The Core Architectures: Transformers and Mamba

Transformers operate on the principle of Self-Attention, which allows the model to process all tokens in a sequence simultaneously to understand their relationships. By calculating pairwise interactions between every word in a sentence, the model can capture complex, long-range dependencies with high precision. However, this comprehensive “look-at-everything” approach comes with a quadratic computational cost (O(L2)), meaning that as the input length increases, the memory and processing power required grow exponentially. This architecture is the gold standard for tasks requiring deep reasoning and precise data retrieval, though it often struggles with efficiency when handling extremely long documents or real-time streaming.

Mamba introduces a more efficient alternative based on Selective State Space Models (SSMs), which process data linearly (O(L)) rather than quadratically. Unlike the Transformer’s massive memory cache, Mamba utilizes a fixed-size hidden state that acts as a compressed version of the entire preceding sequence, allowing it to maintain performance even as the context grows. Its “selective” mechanism enables the model to dynamically decide which information is important to keep and what can be discarded, effectively solving the traditional memory bottlenecks of older recurrent models. This makes Mamba particularly powerful for high-throughput applications, long-context analysis, and environments where fast, constant-time inference is a priority.

Mamba vs. Traditional Transformers
The primary difference between Mamba and traditional Transformers lies in how they process sequence data. While Transformers rely on the Self-Attention mechanism to “attend” to all previous tokens simultaneously, Mamba utilizes S*elective State Space Models (SSMs)* to maintain a compressed, evolving internal state (Mukhammadiev, 2026; IBM, 2025).

Key Architectural Differences

| Feature             | Traditional Transformer                                     | Mamba (Selective SSM)                                      |
| ------------------- | ----------------------------------------------------------- | ---------------------------------------------------------- |
| **Core Mechanism**  | Self-Attention (every token looks at every other token)     | Selective State Updates (recurrent internal state)         |
| **Complexity**      | **Quadratic** *O*(*L*2)—cost quadruples if sequence doubles | **Linear** *O*(*L*)—cost scales proportionally with length |
| **Memory**          | High (grows with context due to KV Cache)                   | Low (fixed-size "hidden state")                            |
| **Best For**        | Precise retrieval and few-shot reasoning                    | Long context, real-time streaming, and high throughput     |
| **Inference Speed** | Slower as sequences get longer                              | Extremely fast and constant per-token latency              |
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Traditional Transformers

Transformers process an entire sequence at once during training. However, during generation (inference), they must store a KV Cache (Key-Value Cache) of all previous tokens to avoid recomputing them, which leads to massive memory consumption for long documents (Mukhammadiev, 2026; Duhan, 2026).

Conceptual Code (Simplified Self-Attention):

import torch
import torch.nn.functional as F

def transformer_attention(query, key, value):
    # query, key, value shape: [batch, seq_len, dim]
    # 1. Compute scores (Every token vs Every token)
    scores = torch.matmul(query, key.transpose(-2, -1)) / (query.size(-1)**0.5)

    # 2. Apply Softmax (Quadratic memory/compute)
    weights = F.softmax(scores, dim=-1)

    # 3. Weighted sum of values
    return torch.matmul(weights, value)
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Mamba (Selective SSM)

Mamba acts more like a “smart” Recurrent Neural Network (RNN). It doesn’t look back at every token; instead, it updates a fixed-size hidden state that acts as a compressed memory (Duhan, 2026; IBM, 2025). Mamba’s “Selection” mechanism allows it to decide which information to keep or discard based on the current input, solving the “forgetting” problem of older RNNs (Mukhammadiev, 2026; OpenReview).

Conceptual Code (Simplified SSM Update):

def mamba_ssm_step(x_t, hidden_state, A, B, C, delta):
    # x_t: current token input
    # 1. Discretization (making continuous math digital)
    # delta is input-dependent, allowing "selection"
    dA = torch.exp(delta * A)
    dB = (delta * B)

    # 2. Update the internal "memory" (Hidden State)
    # This is Linear O(L) because it's a step-by-step update
    new_hidden = dA * hidden_state + dB * x_t

    # 3. Generate output
    y_t = C * new_hidden
    return y_t, new_hidden
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Why Mamba Matters?

In 2026, Mamba-3 has emerged as a production-ready alternative to Transformers, offering up to 5x higher throughput for long-document tasks (Duhan, 2026; VentureBeat, 2026). While Transformers still excel at exact data retrieval (like a database), Mamba is superior for “inference-first” applications where speed and memory efficiency are critical (Mukhammadiev, 2026; Duhan, 2026).

IBM and Mamba!

As of April 2026, IBM has integrated Mamba architecture into its flagship Granite family and specialized Biomedical Foundation Models to improve efficiency and performance for long-sequence tasks.

IBM Granite 4.0 & 4.1 Family

IBM introduced a Hybrid Mamba-2/Transformer architecture in its Granite 4.0 and 4.1 models. This design combines the efficient state-tracking of Mamba with the precision of standard Transformers, leading to a 70% reduction in GPU memory needs and 2x faster inference.

> Disclaimer: based on my own research!

Key Granite models using this architecture include:

  • Granite-H-Small: A 32B parameter model (9B active) designed for heavy-duty enterprise tasks.
  • Granite-H-Tiny: A 7B parameter model (1B active) optimized for low-latency, high-volume tasks.
  • Granite-H-Micro: A 3B parameter dense model tailored for local and edge deployments.
  • Granite-H-Nano: Compact variants (350M and 1B parameters) designed for on-device applications.

IBM Research Biomedical Models

IBM Research utilizes Mamba blocks in specialized models to handle the linear complexity required for genomics and molecular data.

  • biomed.omics.bl.sm.ma-ted-458m: A multi-domain, sequence-based model trained on biologics, small molecules, and single-cell RNA-seq data.
  • Bamba: A research hybrid model developed to fuse SSM efficiency with Transformer-level accuracy for large-scale scientific modeling.
  • Genomics Foundation Models: IBM has analyzed Mamba’s performance in processing over 1 million tokens in genomic sequences, outperforming traditional baselines 
Model Comparison Summary

| Model Series     | Type                     | Primary Benefit                                              |
| ---------------- | ------------------------ | ------------------------------------------------------------ |
| **Granite-H**    | Hybrid Mamba/Transformer | Reduces GPU memory by 70% and doubles inference speed.       |
| **Biomed Omics** | Specialized SSM          | Scales linearly to handle extremely long DNA and protein sequences. |
| **Bamba**        | Research Hybrid          | Combines Transformer accuracy with Mamba's linear scaling for scientific discovery. |
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That’s a wrap! 🫔

Links, References and Citations

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