Mixture-of-Experts (MoE): The Secret Behind DeepSeek, Mistral, and Qwen3

In recent years, large language models (LLMs) have continuously broken records in terms of capabilities and size, with some models now boasting hundreds of billions of parameters. However, a recent trend has enabled these massive models to achieve efficiency simultaneously: Mixture-of-Experts (MoE) layers. The AI community is buzzing about MoE because new models like DeepSeek, Mistral Mixtral, and Alibaba’s Qwen3 leverage this technique to deliver high performance at a lower computational cost. For example, DeepSeek-R1, with an impressive 671 billion parameters, only activates approximately 37 billion of them for any given input, thanks to MoE. Similarly, Mistral AI’s Mixtral 8×7B is a sparse MoE model with 46.7 billion total parameters but uses only about 12.9 billion per token, offering the speed of a 12 billion parameter model while outperforming much larger ones. Even Alibaba’s latest Qwen-3 model family includes MoE variants (up to 235 billion parameters with only 22 billion active) to enhance efficiency.

This post will break down what Mixture-of-Experts is, why it’s making a comeback, and how it works under the hood. We’ll also walk through a simple example (with code) of implementing an MoE-based model step-by-step. By the end, you’ll understand how MoE enables these new AI models to be massive in scale yet only use a fraction of their parameters for each task—much like consulting the right specialist for a job rather than everyone at once.

What is Mixture-of-Experts (MoE)?

At a high level, a Mixture-of-Experts model is akin to having a panel of specialist sub-models and a smart dispatcher that decides which specialists handle each piece of input. Instead of a dense model where every parameter works on every input, MoE is a sparse model where only a subset of parameters (the “experts”) are engaged for a given input. This concept isn’t entirely new—it was first explored in the early 1990s. In a 1991 paper titled Adaptive Mixture of Local Experts, researchers proposed training separate neural networks on different parts of the data and using a gating network to assign each input to the right “expert.” The early MoE system achieved target accuracy in half the training cycles of a conventional network, hinting at the efficiency gains from allowing experts to specialize.

Fast forward to today, and MoE is seeing a resurgence in large-scale AI. The core concept remains the same: break a huge model into smaller pieces that each become highly skilled at certain patterns, and use a gating mechanism to route each input to the most relevant piece. In practical terms, an MoE layer in a transformer might have, say, 8 expert feed-forward networks instead of one. When a token (word) passes through that layer, the model’s router selects the top 1 or 2 experts to handle that token and ignores the rest. This way, the model can have an enormous total number of parameters (summed across all experts), but only activate a few for each token, saving computation and memory.

Key Components of an MoE Architecture

  • Experts: These are the specialized sub-networks (often simple feed-forward networks or other layers) that each input could be routed to. Each expert may learn to handle certain characteristics of the data.
  • Router/Gating Network: This is the “traffic cop” that examines an input (or token) and decides which expert(s) should process it. The router outputs a set of weights or probabilities for the experts, effectively saying, “Expert 3 should handle most of this, maybe Expert 1 a little, and others not at all.”
  • Sparse Activation: Only the experts with the highest gating weights are activated for each input, typically the top-k experts. All other experts remain inactive for that input. This sparsity is what makes MoE efficient—it avoids running the entire network on every example.

In essence, MoE is a form of conditional computation or dynamic ensemble. It’s like a company with many consultants (experts) on staff, but for each project (input), only a couple of the most relevant consultants are called in to work while the rest remain idle. This allows the company to have a wide range of expertise (high capacity) without bearing the cost of deploying everyone on every project.

How Does MoE Work?

The following is a simplified view of an MoE layer. The input X is fed into a Router (gating network with weights W), which computes a hidden vector H(x) and then a SoftMax distribution G(x) over the experts (the bar chart represents the probabilities for four experts). The router selects the top expert(s) based on G(x)—in this illustration, Expert 1 (purple) is the primary activated expert. That expert (a feed-forward network, FFNN 1) processes the input to produce an output E(x). The final output y of the MoE layer is the weighted sum of the expert outputs (here, essentially just Expert 1’s output multiplied by its weight). In practice, often two experts are used per token for added capacity, and their outputs are combined. The other experts (grey) do not run for this input, making the computation sparse and efficient.

Let’s delve deeper into how an MoE layer functions within a model. Imagine we have an MoE layer with multiple expert networks. When an input arrives:

  1. Router Computes Expert Scores: The input is fed into the router (gating network), which produces a score or weight for each expert. These scores are typically converted into probabilities via a SoftMax, so they sum to 1. For example, the router might output weights like [0.75, 0.05, 0.20] for three experts, indicating that Expert 0 is deemed most relevant to this input.
  2. Top Experts Are Selected: Based on the router’s scores, the model selects the top-k experts (often k=1 or k=2) to actually use. In our example, this would be Expert 0 (and possibly Expert 2). The others will be skipped for this input.
  3. Experts Produce Outputs: Each chosen expert processes the input (or token) independently and generates its output (e.g., its prediction or transformation of the data).
  4. Combine Expert Outputs: The outputs of the active experts are then combined, usually by taking a weighted sum using the router’s probabilities. For instance, if Expert 0 has a weight of 0.75 and Expert 2 a weight of 0.20, the final output = 0.75*(Expert 0’s output) + 0.20*(Expert 2’s output) (Expert 1’s 0.05 output is effectively ignored). In many implementations, the top-k experts’ outputs are averaged or added (after weighting) to form the layer’s output, which then proceeds through the rest of the model.

This design means the model’s capacity (total parameters) can be extremely high, while the computation per input remains relatively low. To give a concrete example, consider Mistral’s Mixtral model: it has 8 experts in each layer, totaling approximately 46.7 billion parameters, but only uses about 12.9 billion parameters’ worth of experts for each token. It’s as if the model is a committee of 8 specialists, but any one token only consults 2 of them, so it operates like a ~13 billion-parameter model during inference. This sparse activation is why DeepSeek, Mixtral, Qwen3, and others can scale to hundreds of billions of parameters without a proportional slowdown in inference speed.

Of course, making this work effectively isn’t trivial—the router must learn to send the right inputs to the right experts. If it sends everything to one expert, that expert becomes a bottleneck (and other experts are underutilized). If it scatters inputs randomly, experts don’t specialize. Techniques like load-balancing losses, noise in routing (e.g., Noisy Top-k gating), or adaptive routing (as used by DeepSeek) are employed to ensure all experts are trained and utilized effectively. We won’t delve too deeply into these here, but it’s worth noting that significant research goes into training MoE models effectively and avoiding expert collapse.

A Step-by-Step MoE Notebook Walkthrough

To illustrate MoE in action, we’ll use a simplified scenario: building a text classifier that uses a Mixture-of-Experts for its final prediction. The following is a walkthrough of a notebook (using PyTorch) that implements an MoE model for sentiment analysis on the IMDB dataset. We’ll break it down into steps:

1. Data Preparation and Setup

The notebook begins by loading a dataset and setting up the environment. It uses the IMDB movie reviews dataset for a binary sentiment classification task (positive/negative reviews). Key steps in this phase include:

  • Installing and Importing Libraries: It installs Hugging Face Transformers and Datasets libraries and imports PyTorch. It also checks for a GPU and loads necessary modules (tokenizer, model, etc.).
  • Loading the Dataset: Using datasets.load_dataset, it loads the “imdb” dataset. The IMDB dataset comes with a predefined train/test split. Here, the code uses the training split for training and the test split as a validation set.
  • Tokenization: Since we’re using a transformer model (DistilRoBERTa) as the base, we need to tokenize the text. The code downloads a pre-trained DistilRoBERTa tokenizer and applies it to the text, truncating/padding each review to a fixed length (256 tokens). This is done by mapping a tokenize_function over the dataset, resulting in token IDs and attention masks for each example.
  • DataLoader Preparation: After tokenization, the dataset is converted to PyTorch tensors and loaded into DataLoader objects for batching. The training set is batched (batch size 128) and shuffled, while the validation set is batched (batch size 256) without shuffling. This wraps up the data preparation, yielding iterators we can use in training.

2. Defining the MoE Model Architecture

Next, the notebook defines a custom model class MoEClassifier that implements a Mixture-of-Experts on top of a transformer encoder. Let’s break down this model’s architecture:

class MoEClassifier(nn.Module):
    def __init__(self, base_model_name="distilroberta-base", num_labels=2, num_experts=3):
        super(MoEClassifier, self).__init__()
        # Base transformer model (without classification head)
        self.base_model = AutoModel.from_pretrained(base_model_name)
        self.hidden_size = self.base_model.config.hidden_size
        self.num_labels = num_labels
        self.num_experts = num_experts
        # Expert heads (each a simple linear classifier)
        self.experts = nn.ModuleList([nn.Linear(self.hidden_size, self.num_labels) for _ in range(num_experts)])
        # Gating network that produces weights for each expert
        self.gate = nn.Linear(self.hidden_size, self.num_experts)    
    def forward(self, input_ids, attention_mask, labels=None):
        # Transformer forward pass
        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
        # Use [CLS] token representation (first token) as pooled output
        last_hidden_state = outputs.last_hidden_state  # (batch_size, seq_len, hidden_size)
        pooled_output = last_hidden_state[:, 0, :]  # (batch_size, hidden_size)
        # Compute gating weights (softmax over experts)
        gating_logits = self.gate(pooled_output)  # (batch_size, num_experts)
        gating_weights = torch.softmax(gating_logits, dim=1)  # (batch_size, num_experts)
        # Compute each expert's logits
        expert_logits = torch.stack([expert(pooled_output) for expert in self.experts], dim=1)  # (batch_size, num_experts, num_labels)
        # Combine experts' outputs weighted by gating probabilities
        weighted_logits = expert_logits * gating_weights.unsqueeze(2)  # (batch_size, num_experts, num_labels)
        final_logits = weighted_logits.sum(dim=1)  # (batch_size, num_labels)
        loss = None
        if labels is not None:
            loss_fn = nn.CrossEntropyLoss()
            loss = loss_fn(final_logits, labels)
        # Return a dict similar to HuggingFace model outputs
        return {"loss": loss, "logits": final_logits} if loss is not None else {"logits": final_logits}

  • Base Transformer: The model uses DistilRoBERTa (a smaller variant of RoBERTa) as a feature extractor. In code, AutoModel.from_pretrained(base_model_name) loads the transformer without any classification head. This base model will output a hidden representation for each token in the input text.

  • Expert Layers: On top of the transformer, the MoEClassifier defines multiple expert “heads.” In this implementation, each expert is a simple linear layer that takes the transformer’s hidden state and outputs a logits vector for the classes. If there are num_experts=3 (as set in the code), it creates 3 linear layers of shape [hidden_size -> num_labels] and stores them in a nn.ModuleList called self.experts. In our case (IMDB sentiment analysis), num_labels=2 (positive or negative sentiment), so each expert outputs 2 numbers (logits for “negative” and “positive”). These experts will each attempt to make a prediction.

  • Gating Network: The crucial part—self.gate is another linear layer of shape [hidden_size -> num_experts]. This is the router. Given the transformer’s hidden representation, this layer will produce a set of scores (logits) for each expert. So if there are 3 experts, the gate outputs 3 numbers indicating how much each expert should contribute.

  • Forward Pass Logic: When we call the model on an input, here’s what happens internally:

    • The input text passes through the DistilRoBERTa base model. Suppose the output hidden state is of size (batch_size, seq_len, hidden_size). We take the embedding of the first token (often the [CLS] token) as a pooled representation of the entire sequence. This gives us a tensor pooled_output of shape (batch_size, hidden_size) – one vector per input example.
    • The gating network (self.gate) processes this pooled_output, producing gating_logits of shape (batch_size, num_experts). For each example in the batch, we now have a score for each expert. We apply a SoftMax to these logits to get gating weights (probabilities) for the experts. For instance, an output might be [0.7, 0.2, 0.1] for Experts 0, 1, 2, respectively, meaning Expert 0 is most trusted for that input.
    • Each expert head (the linear layers) is applied to the pooled_output as well. The code does this in one line with a list comprehension: computing each expert’s logits for the input. The result expert_logits can be thought of as a tensor of shape (batch_size, num_experts, num_labels) containing each expert’s prediction scores. For example, Expert 0 might output [-1.2, 2.3] (preferring “positive”), Expert 1 outputs [0.5, 0.1] (preferring “negative”), etc., for a given input.
    • Mixing the Experts: Now the gating weights are used to combine these expert outputs. The code multiplies each expert’s logits by its weight and sums them up: final_logits = sum(weight_i * expert_i_logits). This yields the final prediction logits of shape (batch_size, num_labels) for each input, integrating the contributions of the selected experts. Notably, even though all experts’ logits are computed here, the weighting means if an expert had a very low gating weight, it contributes almost nothing. In practice, one could optimize by computing only top experts, but for simplicity, this implementation computes all and relies on weights to downplay the irrelevant ones.
    • The model can then compute a loss (cross-entropy) if labels are provided during training, and return the final logits (and loss). During inference (no label), it would just output the logits.

In summary, MoEClassifier stacks an MoE layer on top of a transformer: the transformer creates a representation of the input text, and then a gating network decides how to blend the outputs of several expert classifiers to produce the final result. It’s a small-scale illustration of MoE—instead of a huge LLM with dozens of experts per layer, we have 3 experts at the output, each just a linear layer. But the pattern is analogous.

3. Training the MoE Model

With the model defined, the notebook proceeds to train it (albeit just for 1 epoch as a demo). The training loop includes a few standard elements and a couple of interesting points:

# Initialize model, optimizer, and mixed precision scaler
num_experts = 3
model = MoEClassifier(base_model_name=model_name, num_experts=num_experts).to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)
scaler = torch.cuda.amp.GradScaler()# Initialize lists to track losses for plotting
train_losses = []
val_losses = []# Training loop with live plot for loss (both training and validation)
epochs = 1
for epoch in range(epochs):
    model.train()
    total_train_loss = 0.0
    progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch+1}/{epochs}", ncols=100, position=0, leave=True)
    for batch in progress_bar:
        # Move data to device
        input_ids = batch["input_ids"].to(device, non_blocking=True)
        attention_mask = batch["attention_mask"].to(device, non_blocking=True)
        labels = batch["label"].to(device, non_blocking=True)
        optimizer.zero_grad() # Forward pass with mixed precision
        with torch.cuda.amp.autocast():
            outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
            loss = outputs["loss"] # Backward pass and optimization
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
        total_train_loss += loss.item() # Update progress bar with current loss
        progress_bar.set_postfix(loss=total_train_loss / (progress_bar.n + 1)) # Track training loss for live plotting
        train_losses.append(total_train_loss / (progress_bar.n + 1)) # Calculate average training loss
    avg_train_loss = total_train_loss / len(train_dataloader) # Now evaluate on the validation set
    model.eval()
    total_val_loss = 0.0
    with torch.no_grad():
        for batch in val_dataloader:
            input_ids = batch["input_ids"].to(device, non_blocking=True)
            attention_mask = batch["attention_mask"].to(device, non_blocking=True)
            labels = batch["label"].to(device, non_blocking=True) # Forward pass with mixed precision
            with torch.cuda.amp.autocast():
                outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
                loss = outputs["loss"]
            total_val_loss += loss.item() # Calculate average validation loss
    avg_val_loss = total_val_loss / len(val_dataloader) # Track validation loss for live plotting
    val_losses.append(avg_val_loss)
    print(f"Epoch {epoch+1}/{epochs} - Train Loss: {avg_train_loss:.4f} - Validation Loss: {avg_val_loss:.4f}")

  • Model Initialization: They create an instance of MoEClassifier, specifying num_experts=3 (and using the DistilRoBERTa base). The model is moved to the available device (GPU if available). An optimizer (AdamW with a learning rate of 2e-5) is set up on all model parameters.

  • Mixed Precision Training: The code uses PyTorch’s automatic mixed precision (torch.cuda.amp.autocast and GradScaler) to speed up training on GPU by using float16. This is a performance detail—it doesn’t change the MoE logic, but it helps handle the large transformer efficiently.

  • Epoch Loop: They iterate for a number of epochs (in our case, epochs = 1). For each epoch, they set the model to train mode and loop over batches from the train_dataloader. For each batch:

    • Inputs (input_ids and attention_mask) and labels are moved to the device.
    • The model is called inside the autocast context to get outputs and a loss. Under the hood, as we discussed, the model will compute the gating weights, expert outputs, and combined logits for that batch, then compute the cross-entropy loss comparing final_logits to the true labels.
    • The loss is scaled and backpropagated (scaler.scale(loss).backward()), and the optimizer takes a step to update the model weights. This updates not just the transformer weights but also the experts and gating network. Over time, the gating network learns to route inputs, and experts specialize to reduce the loss.
    • They accumulate the training loss to monitor progress.

  • Validation: After the training loop for the epoch, the model is evaluated on the validation set (IMDB test split) to compute an average validation loss. This is done in no-grad mode (no training, just forward passes).

Even with a short training period, the model begins to fit the data. The key takeaway is that training an MoE model isn’t fundamentally different from training a regular model—the main addition is that the gating network and experts’ parameters are learned alongside everything else. In complex MoE models like DeepSeek, there are extra tricks to stabilize training (Google’s MoE research talks about things like added losses for balancing experts, etc.), but conceptually it’s the same: you compute gradients for the selected experts and the router, and update them.

4. Inference: Using the MoE Model (and Interpreting It)

After training, the notebook demonstrates how to use the model for inference on new text. It defines a custom infer() function that not only gets the prediction but also prints out what the MoE is doing internally for transparency. Let’s go through an example:

import torch
import torch.nn.functional as F

def infer(model, input_data):
    model.eval()  # Set model to evaluation mode
    device = next(model.parameters()).device  # Get the device (CPU or GPU) where the model is located
    input_data = input_data.to(device)        # Move the input data to the same device as the model
    # Cast the input to the same data type as the model's weights (usually float32)
    input_data = input_data.to(dtype=torch.long)
    # Use mixed precision for efficiency if available
    with torch.no_grad():
        with torch.cuda.amp.autocast():
            # Step 1: Forward pass through the base transformer model
            outputs = model.base_model(input_ids=input_data)  # Get transformer outputs
            pooled_output = outputs.last_hidden_state[:, 0, :]  # [CLS] token representation
            # Step 2: Gating network produces weights for each expert
            gating_logits = model.gate(pooled_output)  # Raw output of the gating network (logits)
            gating_weights = F.softmax(gating_logits, dim=-1)  # Convert to probabilities that sum to 1
            # Step 3: Each expert produces logits for the input
            expert_outputs = [expert(pooled_output) for expert in model.experts]  # List of tensors (one per expert)
            # Step 4: Combine expert outputs using gating weights
            combined_logits = torch.zeros_like(expert_outputs[0])
            for w, logits in zip(gating_weights[0], expert_outputs):
                combined_logits += w * logits  # Weighted sum of experts' logits
    # Move tensors to CPU for printing (convert to float for clarity if in half precision)
    gating_weights_cpu = gating_weights[0].detach().cpu().float()   # 1D tensor of length num_experts
    expert_logits_cpu = [logit.detach().cpu().float() for logit in expert_outputs]  # List of tensors
    combined_logits_cpu = combined_logits.detach().cpu().float()    # Tensor of shape (num_classes,)
    # Determine the final predicted label ("positive" or "negative" assuming binary classification)
    pred_index = int(torch.argmax(combined_logits_cpu))  # Index of the highest logit
    prediction_label = "positive" if pred_index == 1 else "negative"
    # Print a detailed explanation of the MoE inference process
    # Explain gating weights
    gw_list = gating_weights_cpu.tolist()
    print(f"Gating weights (for each expert): {gw_list}")
    print(" - The gating network assigns these weights to the experts based on input relevance.")
    for i, w in enumerate(gw_list):
        print(f"   Expert {i} weight: {w:.4f} (proportion of contribution from Expert {i})")
    # Explain each expert's logits and individual prediction
    for i, logits in enumerate(expert_logits_cpu):
        logits_list = logits.tolist()
        print(f"Expert {i} logits: {logits_list}")
        if len(logits_list) == 2:
            # If binary classification, determine which class this expert favors
            neg_logit, pos_logit = logits_list[0], logits_list[1]
            expert_pred = "positive" if pos_logit > neg_logit else "negative"
            print(f" - Expert {i} would predict '{expert_pred}' ("
                  f"{'pos' if expert_pred == 'positive' else 'neg'} logit is higher).")
    # Explain combined logits calculation
    comb_list = combined_logits_cpu.tolist()
    print(f"Combined logits (weighted sum of experts): {comb_list}")
    print(" - Each combined logit is calculated by summing the experts' logits multiplied by their gating weights.")
    if len(comb_list) == 2:
        print(f"   (For example, combined_neg_logit = "
              f"{' + '.join([f'{w:.4f}*{expert_logits_cpu[i][0].item():.4f}' for i, w in enumerate(gw_list)])})")
        print(f"   (And combined_pos_logit = "
              f"{' + '.join([f'{w:.4f}*{expert_logits_cpu[i][1].item():.4f}' for i, w in enumerate(gw_list)])})")
    # Explain final prediction
    print(f"Final Prediction: {prediction_label.upper()}")
    print(f" - The model predicts 'positive' sentiment because that class has the highest combined logit.")
    # Prepare the result dictionary
    result = {
        "expert_logits": [logit.tolist() for logit in expert_logits_cpu],
        "gating_weights": gw_list,
        "combined_logits": comb_list,
        "prediction_label": prediction_label
    }
    return result

# Example input text
sample_text = "I loved the movie! It was so exciting and fun!"# Tokenize the input text
inputs = tokenizer(sample_text, return_tensors="pt", padding=True, truncation=True, max_length=256)# Use the infer function for inference
result = infer(model, inputs["input_ids"])# Display the result (dictionary returned by infer function)
print(result)

Suppose we input the text: “I loved the movie! It was so exciting and fun!” (which is a positive sentiment). The infer function tokenizes this text and feeds it to the model. Here’s what happens and what the function outputs:

  • Gating Weights: The function prints the gating network’s weight for each expert for this input. In one run, it showed something like: Gating weights (for each expert): [0.7612, 0.0801, 0.1587]. This means Expert 0 was assigned ~76% of the responsibility, Expert 1 about 8%, and Expert 2 about 16%. The interpretation is that the router judged Expert 0 to be by far the most relevant for this particular review, Expert 2 somewhat relevant, and Expert 1 not much. (These numbers are specific to this model instance—if we trained longer, they might stabilize differently, but this is the router’s “opinion” after our short training.)
  • Expert Outputs: Next, it shows each expert’s raw logits (scores) for the negative vs positive classes. For example:

    • Expert 0 logits: [-2.5215, 2.2305]
    • Expert 1 logits: [-0.5479, 1.1855]
    • Expert 2 logits: [-1.2412, 1.9307]

These are the outputs of each expert’s linear layer on the [CLS] embedding. In binary classification, a higher second number indicates a vote for “positive.” We can see Expert 0 strongly votes positive (since 2.23 ≫ -2.52), Expert 1 also leans positive (1.185 > -0.548), and Expert 2 leans positive as well (1.931 > -1.241). All three experts are actually indicating “this is probably a positive review,” though Expert 0 is the most confident.

  • Combined Logits: The function then prints the combined logits after weighting the experts: Combined logits: [[-2.1602, 2.0996]]. How was this computed? It’s essentially 0.7612 * Expert0_logits + 0.0801 * Expert1_logits + 0.1587 * Expert2_logits. If you do the math for the positive logit: 0.7612 × 2.2305 + 0.0801 × 1.1855 + 0.1587 × 1.9307 ≈ 2.10, which matches the combined positive logit. The function even prints out a breakdown formula for the combination to make this clear. The combined negative logit is similarly calculated as the weighted sum of the experts’ negative logits.
  • Final Prediction: Finally, it outputs the model’s predicted label: POSITIVE. This makes sense because the combined logits show the positive class has the higher score (2.0996 > -2.1602). The function also explains: “The model predicts ‘positive’ sentiment because that class has the highest combined logit.” We’ve confirmed that the MoE model correctly classified the example as positive.

What’s cool is that we got a peek into the MoE’s decision-making process. We saw that Expert 0 carried most of the weight for this input, and indeed Expert 0 had a very strong positive sentiment signal, which drove the final prediction. Expert 1 didn’t contribute much (its weight was low), and even if Expert 1 had been wrong, it wouldn’t have mattered much. This is exactly how MoE provides robustness and efficiency: if one expert is confident and appropriate, the router leans heavily on it. If a different kind of input came in, say a very nuanced or negative review, Expert 2 might get a higher weight and take charge. This is precisely how MoE achieves robustness and efficiency—if one expert is confident and suitable for a particular input, the router heavily relies on it. If a different type of input arrives, such as a nuanced or negative review, Expert 2 might receive a higher weight and take over. For instance, Expert 2 might be more adept at handling complex emotional expressions or sarcasm. In such cases, the router directs the input to Expert 2, allowing it to leverage its specialized skills to make a more accurate judgment. This dynamic allocation of tasks ensures that each expert focuses on what it does best, thereby improving the overall performance and adaptability of the model.

Conclusion

Mixture-of-Experts (MoE) is an exciting architectural pattern that enables AI models to scale horizontally by adding more “brains” (experts) without slowing down each inference step. By activating only a few experts per input, MoE models like DeepSeek, Mixtral, and Qwen3 achieve the best of both worlds: extremely high capacity and specialization, coupled with efficiency comparable to much smaller models.

In this post, we’ve introduced the MoE concept and walked through a toy example of how an MoE model functions. We’ve seen how a router network can learn to distribute tasks among expert sub-networks and how the outputs are combined to produce a final answer. This approach is driving some of the latest breakthroughs—DeepSeek R1’s 671-billion-parameter model uses MoE to utilize only ~37 billion parameters per token, and Qwen-3’s MoE models handle complex reasoning by delegating sub-tasks to different experts.

As research continues, we’re likely to see MoE used in even more creative ways (and with improvements to training stability and expert balancing). For anyone building or exploring AI models, MoE is a valuable technique when you need to scale a model’s capacity without breaking the compute bank. After all, sometimes it’s smarter (and faster) to use a team of specialists rather than a single jack-of-all-trades model!

References