Deploying Massive MoE Models: MXFP4 Storage, 104B Activations, and High-Concurrency Inference Analysis

1. Background: From Model Release to Open Weights

Recently, a massive Mixture-of-Experts (MoE) model with approximately 2.8T total parameters and around 104B activated parameters per token officially released its complete weights. As per the release plan, the weights were published to public model repositories ahead of schedule for research, deployment, fine-tuning, and auditing. Notably, this model ranked 1st among open-weight models in the Agent Arena and 13th on the overall leaderboard.

This result reflects a broader trend: large model competition is no longer solely about parameter scale or general conversational ability, but increasingly emphasizes agentic tasks, tool calling, long-context reasoning, and multimodal inputs. For enterprise developers, open weights mean that inference, fine-tuning, safety audits, and compliance modifications can be executed in self-hosted environments or private clouds, rather than being restricted to public APIs.

However, open weights do not mean out-of-the-box readiness. With roughly 2.8T total parameters, a repository size of about 1.56TB, and the use of MXFP4 weight storage combined with MXFP8 activations, a clear evaluation of VRAM, bandwidth, parallelism strategies, and inference framework compatibility is mandatory before deployment.

---

2. What You Get with Open Weights

2.1 Downloadable Assets

The model weights are distributed in the safetensors format, with the repository occupying approximately 1.56TB and containing an estimated 2.78 trillion parameters. Compared to original FP16 or BF16 weights, this footprint is significantly compressed.

Official documentation states that weights are stored in MXFP4 and activations in MXFP8, utilizing Quantization-Aware Training (QAT). In other words, the model was not simply post-training compressed; the impact of low-precision representation on loss, gradients, and inference stability was accounted for during the training phase. This approach minimizes model quality degradation while reducing weight size.

Upon downloading, developers can access the model card’s structural description, including the number of layers, attention layer types, expert count, vision module, and context length. This information is critical for subsequent VRAM estimation and parallelism partitioning.

2.2 Model Architecture Overview

According to the model card, the architecture consists of 93 layers: 69 KDA layers and 24 Gated DSA layers. Compared to the fully attentive Gated MLA structure in the previous generation, the new design replaces certain layers with a sparse attention structure to reduce the overhead of long-context modeling.

The expert component comprises 896 experts, activating 16 per token, alongside 2 shared experts. The vision module is MoonViT-V2 (approx. 401M parameters), enabling image-text inputs. The context length can scale up to 1 million tokens, which means that VRAM consumption and inference latency require special attention when processing long documents, code repositories, or agent trajectories.

2.3 How 2.8T Parameters Yield 104B Activations

This is the core concept of the MoE architecture: the total parameter scale is massive, but only a subset of experts is activated during each forward pass. For a given token, the model selects 16 activated experts out of 896, plus shared experts. The actual parameters involved in computation are far smaller than the total scale.

Consequently, the model strikes a balance between large-scale knowledge capacity and per-token computation cost. The total parameter volume benefits model capacity and knowledge retention, while sparse activation controls compute and VRAM bandwidth consumption per step. Engineers must note that while the activated parameters are 104B, all expert weights still need to be loaded into VRAM or a fast-access storage tier. Activated parameter count cannot be directly equated to VRAM requirements.

---

3. Low-Precision Training and Inference Format Analysis

3.1 Understanding MXFP4 Weights and MXFP8 Activations

MXFP refers to microscaling floating-point formats, primarily applied for low-precision representation in training and inference. Unlike standard INT4 or FP8, MX formats use shared scaling factors to reduce per-element bit width while maintaining a certain dynamic range.

In this model:

• Weights are stored in MXFP4, reducing disk and VRAM footprint.
• Activations are represented in MXFP8, balancing computation precision and forward propagation stability.
• QAT ensures the model retains near-original quality under low-precision representation.

During deployment, arbitrary conversion to other precisions is discouraged. Converting weights to FP16 would double the size, and using unsupported inference engines might lead to precision handling errors or unsupported operator issues.

3.2 Why Not Standard INT4?

Compared to standard INT4 quantization, MXFP4 is better suited for open-weight scenarios of massive models. Standard INT4 has a limited numerical range, relies on scaling and zero points, and is prone to precision degradation in long-tail distributions or outlier activations. By sharing exponents or scaling factors, MXFP4 preserves a wider dynamic range, making the impact on model quality more controllable under QAT.

For deployment teams, the key is not debating the theoretical differences between MXFP4 and INT4, but verifying whether the inference framework natively supports the corresponding low-precision operators, de-quantization paths, and batching strategies.

3.3 Repository Size vs. Parameter Count

The repository is roughly 1.56TB for about 2.78 trillion parameters. Estimated in FP16, 2.8T parameters would require over 5.5TB of VRAM or disk space. The current 1.56TB size demonstrates significant compression and format optimization.

However, note that 1.56TB is the storage size, not the single-node VRAM requirement. Inference must also account for activations, KV cache, expert buffers, intermediate communication states, and runtime overhead. It is inaccurate to assume that “having 1.56TB of VRAM is enough to run inference.”

---

4. VRAM and Hardware Planning for Local Deployment

4.1 Basic VRAM Estimation Logic

For an MoE model of this scale, VRAM requirements can be broken down into several components:

1. Weight VRAM: ~1.56TB, depending on whether it is loaded directly in MXFP4.
2. Activation VRAM: Determined by batch size, sequence length, and activation precision.
3. Long-context Cache: KV cache grows significantly at a 1-million-token context length.
4. Expert Routing Cache: MoE expert selection and intermediate states consume VRAM.
5. Framework Runtime Overhead: Graph caches, operator fusion, and communication buffers.

Even if the weights require around 1.56TB, the actual total VRAM needed in practice is typically higher. Redundancy and fragmentation during multi-GPU parallelism must also be considered.

4.2 A Viable Hardware Configuration

Third-party estimates suggest that a single node equipped with 8 B300-level accelerators could provide a total of ~2.3TB VRAM, costing approximately $550,000 to $650,000. This configuration is suited for research institutions, foundational model teams, or high-performance computing platforms, not standard development environments.

Note that these are third-party estimates; there is no official hardware recommendation. Optimal hardware choices vary significantly based on use cases:

• High-throughput online services focus on concurrent requests and VRAM utilization.
• Batch offline tasks prioritize long-context stability and throughput.
• Fine-tuning tasks require additional VRAM for optimizer states, gradients, and parameter copies.

4.3 Multi-GPU Parallelism and Communication Pressure

A model of this scale cannot run on a single GPU. It typically requires a hybrid of Tensor Parallelism (TP), Pipeline Parallelism (PP), and Expert Parallelism (EP). MoE models also involve expert routing, where different tokens select different experts, creating unique communication patterns.

For instance, under EP, each device stores a subset of expert weights, and routing requires cross-device transmission of token hidden states. Communication pressure increases with long contexts, large batches, or multi-turn agent tasks. Engineers must focus on high-speed interconnects, topology, gradient synchronization, and expert load balancing.

---

5. Inference Frameworks and Deployment Recipes

5.1 Framework Adaptation

Public information indicates that several mainstream inference frameworks now offer deployment recipes. These recipes typically include:

• Weight loading methods;
• MXFP4/MXFP8 de-quantization configurations;
• TP and PP settings;
• MoE expert partitioning;
• Long-context caching strategies;
• Batching and concurrency parameters;
• Sampling parameters and stopping conditions.

Deployment teams can reference these configurations but should not copy them blindly. Optimal settings vary depending on hardware topology, request distribution, and business metrics.

5.2 Recommended Deployment Workflow

A standard deployment workflow involves the following steps:

1. Evaluate Weight Media and Storage: Prepare high-speed object storage or a distributed file system to host the ~1.56TB weights.
2. Verify Compute Node VRAM: Prioritize environments with 2TB+ VRAM capacity.
3. Install and Adapt Inference Framework: Enable MXFP4 and MXFP8 support.
4. Build a Small-Scale Validation Environment: Verify model output consistency using short sequences and small batch sizes.
5. Tune Parallelism Parameters: Optimize TP, PP, and EP based on target throughput and latency.
6. Stress Testing and Stability Validation: Pay special attention to cache expansion and memory peaks under 1-million-token scenarios.
7. Integrate into Production: Configure health checks, circuit breakers, rate limiting, and monitoring alerts.

In large enterprises or cloud environments, these steps should be managed via container platforms, image repositories, logging services, and unified monitoring systems.

---

6. API Integration: When Self-Hosting Isn’t Viable

For most business teams, self-hosting a 2.8T parameter model is not cost-effective. A more common approach is utilizing cloud inference services or managed APIs. The model provides APIs compatible with OpenAI and Anthropic interfaces, under the model name kimi-k3.1-pro. A high-speed version is also available, which official sources claim is 2 to 3 times faster than the standard version.

6.1 Sampling and Inference Parameters

The API offers a reasoning_effort parameter set to low, high, or max (default is max). This controls reasoning depth, thinking token count, and response quality:

• Low: Suited for simple queries, classification, and intent recognition.
• High: Suited for mathematical reasoning, complex code debugging, and agent planning.
• Max: May generate longer thinking content and higher latency.

Developers should select the appropriate reasoning effort based on the business scenario rather than defaulting to the highest setting.

6.2 State Passing in Multi-Turn Tool Calls

In agentic tasks, multi-turn tool calling requires passing the previous round’s reasoning content and tool call results back to the model. Ignoring the reasoning content or tool call structure can cause the model to lose prior decision logic.

It is recommended to maintain a structured message state on the client side, containing at least:

• User input;
• Model replies;
• Previous reasoning content;
• Tool call identifiers and parameters;
• Tool return results;
• Next round user or environment input.

A simplified pseudo-code example:

messages = [
    {"role": "user", "content": "Analyze this log file and provide anomaly conclusions"}
]
for step in range(max_steps):
    response = client.chat(
        model="kimi-k3.1-pro",
        messages=messages,
        reasoning_effort="max",
        tools=tools,
    )
    messages.append(response.message)
    if response.message.tool_calls:
        for tool_call in response.message.tool_calls:
            tool_result = execute_tool(tool_call)
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": tool_result,
            })
    else:
        break

Note that different API versions may handle the naming and passing of reasoning_content and tool_calls differently. When building a unified integration layer, retain these fields rather than extracting only the final text. Otherwise, the model may lose its reasoning chain regarding the initial problem during multi-turn tool calls.

6.3 Standard vs. High-Speed Versions

The high-speed version is faster, making it suitable for real-time interactions, online assistants, or high-concurrency scenarios. The standard version is better for tasks requiring higher output quality where latency is less sensitive. Official sources have not disclosed whether the high-speed version achieves speedups via reduced reasoning effort, lower parameter precision, or altered sampling strategies. Developers should test with real business traffic before deciding which version to use.

---

7. Engineering Impact of Long Context and Multimodal Inputs

7.1 Challenges of 1-Million-Token Context

A 1-million-token context is valuable for codebase understanding, long report analysis, meeting summarization, and complex agent tasks, but it introduces significant pressure:

• Massive key-value states need to be cached during inference;
• Attention computation costs scale with sequence length;
• Long contexts may amplify load imbalance in MoE routing;
• Increased Time-to-First-Token (TTFT);
• Expanded error propagation, where the model might overfocus on irrelevant information.

Therefore, local deployment evaluations must include peak VRAM and latency under maximum context length, not just weight loading. For API integration, consider whether the entire document must be input at once, or if retrieval, segmentation, or hierarchical summarization can be used to reduce context length.

7.2 Multimodal Input Pipeline

The vision module is MoonViT-V2 (~401M parameters). Input images must undergo preprocessing and visual encoding before fusing with text tokens. Developers need to verify:

• Support for single vs. multiple images;
• How image size and resolution affect visual token count;
• How visual tokens are concatenated with text tokens;
• Whether visual encoding consumes additional VRAM and latency;
• Support for vision module parallelism and caching in private deployments.

For enterprise knowledge base scenarios, a common practice is to offline extract image descriptions or layout information to reduce online visual encoding frequency. However, complex documents may still require the model to directly understand the raw layout.

---

8. Comprehensive Recommendations: From Open Weights to Production

8.1 Determine if Self-Hosting is Necessary

Self-hosting is only recommended if:

• Data security and compliance mandate privatization;
• Large-scale fine-tuning for specific domains is required;
• Deep auditing of model weights and training data is necessary;
• An existing large heterogeneous compute cluster can cover operational costs;
• Low-level performance optimization of the inference pipeline is required.

Otherwise, prioritizing managed APIs or cloud inference services is a more maintainable solution.

8.2 Cloud Deployment and Operations

If deploying on the cloud, use general-purpose container platforms for inference service orchestration. Combine this with object storage for weights, logging services for runtime logs, and monitoring systems for latency and VRAM tracking. Multiple inference instances can scale throughput via load balancing.

Key focus areas include:

• Weight download and image build time;
• Cold start time for massive model instances;
• Auto-scaling strategies;
• Request queuing, timeout, and retry policies;
• Resource release for long connections;
• Weight synchronization across availability zones.

8.3 Balancing Performance and Quality

The appeal of open-weight models lies in customizability and auditability. However, teams should establish performance baselines based on business goals rather than blindly maximizing reasoning effort or context length. For instance, in customer service scenarios with strict response time limits, a lower reasoning effort is more appropriate than the maximum setting. Conversely, code refactoring or complex numerical computations can accommodate longer thinking times.

---

9. Conclusion

The release of open weights for this ~2.8T parameter, 104B activation MoE model provides researchers, infrastructure teams, and large enterprises with direct model access. MXFP4 weights, MXFP8 activations, 1-million-token context, multimodal inputs, and strong agentic capabilities collectively define the technical characteristics of this generation of models.

However, the deployment barrier remains high. The ~1.56TB weight footprint, complex MoE parallel scheduling, long-context caching, and low-precision inference adaptation make it best suited for specialized infrastructure teams. Standard business teams are better off integrating via APIs, focusing their efforts on prompt engineering, tool calling strategies, result validation, and business logic.

For engineers, understanding weight formats, activation scales, VRAM architecture, and inference parameters is far more critical to system success than simply pursuing the “largest parameter model.” Open weights are just the starting point; achieving stable, low-cost operation in production is the core challenge.