1. Background: The Parameter Race Meets the Compute Wall

Large model capabilities largely follow scaling laws: more parameters and more data lead to greater knowledge capacity and higher reasoning ceilings, with emergent abilities appearing beyond a certain scale. Over the past few years, the industry has pushed model parameters from tens of billions to hundreds of billions and now into the trillion range.

For traditional dense architectures, however, parameter count and compute cost are coupled: a trillion-parameter dense model must engage all weights for every token it processes, making training and inference costs scale linearly—and deployment practically infeasible. The core tension is clear: everyone wants a trillion-scale “knowledge base” but is only willing to pay the electricity bill of a few tens of billions of parameters.

Mixture-of-Experts (MoE) answers this with sparse activation: it fully decouples “how much knowledge is stored” from “how much is computed per pass.” This is precisely why nearly all publicly available trillion-scale models today adopt the MoE approach.

A note on naming culture: many teams like to name their models after famous peaks, symbolizing the ambition to climb the hardest mountains. For large model technology, “trillion-parameter MoE” and “reliable agentic capabilities” are widely recognized as two such peaks. This article examines both climbing routes.

2. Deconstructing the MoE Architecture

2.1 Replacing a Single FFN with an “Expert Committee”

In a standard Transformer block, each layer consists of an attention layer and a feed-forward network (FFN), with the FFN holding most of the model’s parameters. MoE replaces the FFN with N structurally identical but independently parameterized “expert” FFNs, plus a gating network (Router).

When each token flows through the layer, the Router computes match scores between the token and each expert, dispatching the token only to the top-scoring few experts while the rest contribute zero computation. Think of hospital triage: a hospital may have thousands of doctors (total parameters), but each patient only visits two or three departments (activated experts) rather than convening the entire hospital—vast total knowledge, small per-visit cost.

2.2 Sparse Activation: Trillion-Scale Capacity, Tens-of-Billions Compute Bill

Mainstream trillion-scale MoE configurations typically exhibit the following characteristics:

• Total parameter count in the trillion range (~1T), determining the model’s knowledge ceiling;
• Only tens of billions of parameters activated per token during inference (commonly in the 20B–40B range), an activation ratio of roughly 3%;
• Routing to a small number of selected experts (e.g., 6–8) plus 1 shared expert for collaborative processing.

This means per-token compute is comparable to a dense model of a few tens of billions of parameters, while learnable knowledge capacity is amplified by dozens of times. The same inference budget can “hold” far more world knowledge and skills.

2.3 Shared Experts: No Redundant Work for Common Knowledge

Substantial common computation exists across different tokens and tasks—basic grammar, general semantics, common reasoning patterns. If every expert had to learn these shared capabilities independently, parameters would be heavily redundant. With shared experts:

• Shared experts apply to all tokens and are responsible for accumulating common patterns;
• Selected experts focus on differentiated knowledge such as code, mathematics, multilingual capabilities, and tool-calling patterns.

This “common + specialized” division of labor significantly improves parameter utilization and has been an important direction in recent MoE design.

2.4 Routing and Load Balancing: The Details That Determine MoE Success

• Top-K routing: take the top K scores, with all other experts contributing zero—this is the direct source of sparsity;
• Routing collapse: without constraints, the Router concentrates traffic on a few “star experts,” leaving the rest untrained and degrading model capability;
• Load balancing: during training, an auxiliary balancing loss is typically introduced to penalize uneven expert load, combined with capacity factors, overflow strategies, and other engineering measures to keep experts “busy but orderly”;
• Expert parallelism: distributing different experts across different devices during training and inference, where cross-device all-to-all communication is one of the main system overheads.

A schematic pseudocode snippet to illustrate the routing and weighted merge process:

# Schematic pseudocode: minimal MoE FFN layer (Top-K routing + shared experts)
def moe_ffn(x, experts, shared_experts, gate, top_k=8):
    # x: [batch, seq_len, d_model]
    scores = softmax(gate(x), dim=-1)              # match score of each token against all experts
    topk_score, topk_id = topk(scores, k=top_k)    # keep only the K highest-scoring experts
    topk_score = topk_score / sum(topk_score)      # normalize gating weights
    out = zeros_like(x)
    for e, expert in enumerate(experts):
        pos = where(topk_id == e)                  # find tokens that selected this expert
        if pos is empty: continue
        y = expert(x[pos])                         # forward computation only for these tokens
        out[pos] += y * topk_score[pos]            # weighted merge by gating weights
    for se in shared_experts:                      # shared experts apply to all tokens
        out += se(x)
    return out

2.5 From Which Layer to Introduce MoE? An Engineering Trade-off

Not all layers are suitable for sparsification. Two common approaches exist: full MoE, or keeping shallow layers dense and introducing MoE from the second block onward. The rationale for the latter: shallow layers are closer to general feature extraction, and keeping them dense helps stabilize lower-level representations; middle and upper layers handle more semantic differentiation, and introducing sparse structure early enough there captures the benefits of expert specialization while controlling overall compute. There is no gold standard for which layer to start from—it is fundamentally a trade-off among capacity, stability, and compute budget.

3. Engineering Realities of Training and Inference

3.1 Three Challenges on the Training Side

1. Stability: routing is a discrete selection, and gradients can only flow back through gating weights. Training tends to be unstable, requiring careful learning rate schedules, balancing loss coefficients, and initialization schemes;
2. Communication: expert parallelism introduces all-to-all communication that is extremely sensitive to interconnect bandwidth, with cluster topology directly affecting throughput;
3. Memory: although activated parameters are few, all parameters must reside in memory (or be offloaded in tiers)—the “storage cost” is not low.

3.2 Two Cost Accounts on the Inference Side

Evaluating MoE inference cost requires two accounts: the memory account looks at total parameters, and the compute account looks at activated parameters. Two corollaries follow:

• In high-concurrency, large-batch server scenarios, different tokens activate different experts, leading to high hardware utilization and fully amplifying MoE’s advantages;
• In very low-concurrency, single-request scenarios, the compute savings from sparsity are partially offset by memory costs.

Trillion-scale MoE models are therefore especially well-suited for throughput-sensitive workloads such as large-scale online serving and long-chain agent invocations.

4. From “Answering Questions” to “Getting Things Done”: The Agentic Paradigm Shift

4.1 Division of Labor Between Base Models and Agent Models

A complete model system typically includes two tiers:

• Base model: acquires language, knowledge, and general reasoning through large-scale pretraining—the “brain” of the system;
• Agent/chat model: built on top of the base model, further trained on massive agent trajectory data (task decomposition, tool calling, self-verification, and reflection) plus reinforcement learning, ultimately producing a model that “thinks while using tools.”

The two stand in a “foundation” and “behavior” relationship: with only the foundation, a model is knowledgeable but cannot act; with only behavior templates and insufficient knowledge and reasoning depth, an agent is an empty shell.

4.2 Chain-of-Thought Is Not the Same as an Agent

• Chain-of-Thought (CoT): reasoning unfolds statically inside the model—“think it through, then answer”—with no interaction with the external world throughout;
• Agentic paradigm: a closed loop of think → act → rethink. The model perceives feedback in a dynamic environment, plans steps, calls tools (retrieval, computation, code execution, business APIs, etc.), and adjusts its next strategy based on observations.

The key difference lies in the word “closed loop.” CoT correctness depends entirely on parametric knowledge and is powerless against real-time information, private data, or precise computation; agents can use tools to obtain new information and perform real actions, breaking through the temporal and scope limitations of static training data.

4.3 Reinforcement Learning Teaches Models “When to Think, When to Act”

In agent training, reward signals come from whether a task is actually completed, not whether a sentence is fluently worded. This forces the model to learn strategy-level capabilities: when it is worth thinking a few more steps, when to call a tool directly, and how to retry or switch paths after a tool error. Such “process capabilities” are hard to exhaustively cover with human-annotated data—precisely where reinforcement learning excels.

4.4 A Minimal Agent Execution Loop

# Schematic pseudocode: minimal agent execution loop
def agent_loop(task, tools, llm, max_steps=20):
    history = [build_system_prompt(task)]
    for step in range(max_steps):
        thought = llm.think(history)               # think: decompose the problem, plan the next step
        action  = llm.propose_action(history)      # act: choose a tool and generate call arguments
        if action.name == "finish":
            return action.answer
        observation = tools[action.name].run(action.args)   # interact with the environment
        history += [thought, action, observation]           # rethink: feedback enters context
    return llm.summarize(history)                   # fallback output when step limit is reached

Real systems add memory management, multi-agent collaboration, sandboxed execution, and result verification on top of this, but the core loop remains unchanged.

4.5 Why Trillion-Scale MoE Naturally Fits Agents

A single complex task often involves dozens of rounds of “think + call,” consuming more than ten times the tokens of ordinary Q&A. Sparse activation keeps the cost of long-chain tasks manageable; trillion-scale parameter capacity lets the model “see” richer tool combinations and domain patterns. One is responsible for computing cheaply, the other for thinking deeply—together they form the de facto standard for today’s high-performance agents.

5. Selection and Practical Advice for Developers

1. Task matching: simple classification, extraction, and short-text generation are well served by dense models of a few tens of billions of parameters; for complex tasks like multi-step reasoning, tool orchestration, and code generation, prioritize evaluating MoE agent models;
2. Cost estimation: model based on “per-token activation cost × total task token consumption,” not parameter scale alone; for agent scenarios, always include multi-round invocations in the budget;
3. Integration path: building a trillion-scale inference cluster in-house has an extremely high barrier; a more realistic path is to access cloud-based large model services. For example, Baidu AI Cloud provides general-purpose capabilities such as large model services, agent development platforms, and model fine-tuning and deployment toolchains, which can directly handle model invocation, workflow orchestration, and performance evaluation—letting engineering effort stay focused on the business itself;
4. Evaluate first: use real task sets from your own business to validate tool-calling success rates and multi-step task completion rates; benchmark scores are only a reference and cannot replace business evaluation.

6. Conclusion

Trillion-parameter MoE answers “how to know a lot while computing cheaply,” and the agentic paradigm answers “how to think deeply and get things done.” The former reshapes the cost curve of model architecture through sparse activation and expert routing; the latter redefines how models interact with the world through the closed loop of “think—act—rethink.” Naming models after peaks expresses climbing ambition, but what truly determines a model’s height is the down-to-earth engineering work: sparse architecture, routing details, load balancing, and reinforcement learning. For developers, understanding these mechanisms is what enables more professional judgment in model selection, cost control, and agent application design.