Day-0 Adaptation for a 3T-Parameter Multimodal Model: Inference Optimization on Heterogeneous Compute

As large models surpass the trillion-parameter threshold and demonstrate strong capabilities in code generation, long-horizon agent tasks, and multimodal understanding, the challenge of adapting underlying compute infrastructure has become increasingly severe. Recently, an open-weight model released a new version with up to 3 trillion parameters, supporting million-token context windows and achieving results on various benchmarks that match or approach top-tier closed-source models. However, achieving Day-0 deployment and stable inference for such ultra-large models across diverse hardware architectures remains a core pain point for developers and enterprises. This article sets aside vendor-specific branding and analyzes, from a general technical perspective, the adaptation strategies and engineering practices for deploying ultra-large models on heterogeneous compute.

I. Engineering Challenges of Trillion-Parameter Inference

For a model with 3 trillion parameters, inference compute faces not only the physical limits of memory capacity but also stringent demands on data movement, cross-card communication, and computational throughput. Such models typically employ mixture-of-experts (MoE) architectures and ultra-long context mechanisms, leading to engineering difficulties including severe memory fragmentation, rapid KV cache expansion with context length, and communication bottlenecks.

To achieve Day-0 adaptation—loading weights, starting services, and establishing a basic inference pipeline on the first day a new model is released—teams must rely on a highly compatible, highly abstracted low-level software stack. This stack must cover the full chain from framework execution and operator compilation to device runtime and multi-card communication. By reusing mature interfaces and performing targeted optimization for new compute paths, community-standard model code can be rapidly transformed into a working inference service on specific hardware.

II. High-Compatibility Software Stack Design and Migration Strategy

As large models iterate at an accelerating pace, traditional hard-coded low-level adaptation can no longer meet rapid deployment requirements. Modern intelligent compute card software stacks (such as the industry-standard MUSA architecture) provide high compatibility at the programming model, runtime interface, and AI framework levels, serving as an important foundation for rapid migration.

1. Framework Layer via SGLang

At the framework level, mainstream inference frameworks such as SGLang have become standard for long-context model deployment, thanks to their efficient RadixAttention mechanism and strong serving throughput. By adapting SGLang, teams can directly reuse upstream open-source model implementations, avoiding the massive effort of building an inference engine from scratch. The decoupling of the framework layer from underlying hardware allows model computation graph construction and hardware operator mapping to proceed in parallel.

2. Operator Library and Compiler Co-Optimization

For migrating key compute paths of new architectures, teams need to build a multi-layered operator support system:

• Base operator library (e.g., MATE): Provides highly optimized standard Attention, FFN, and other foundational compute kernels, covering the vast majority of routine computation needs.
• Triton-like DSL and TileLang: Through high-level domain-specific languages (DSLs), developers can write custom operators with minimal overhead. Compilers (e.g., MTCC) translate these DSL-described operators into efficient machine code for specific hardware platforms.
• Compiler auto-tuning: Across different batch sizes and sequence lengths, the underlying compiler can automatically explore optimal thread block partitioning and memory access strategies, greatly shortening the development cycle for new operators.

III. Deep Integration and Optimization of Core Compute Paths

After completing basic framework integration, deep adaptation for model-specific architectures is key to improving inference efficiency. This adaptation effort faced two major technical challenges: integrating sparse attention (DSA) and handling MXFP4-precision expert weights.

1. Integrating DSA: A Complete Loop for Hybrid Attention and Vision Compute

To address KV cache explosion caused by ultra-long contexts, the 3-trillion-parameter model adopts a sparse attention mechanism (DSA). The adaptation team needed to perform deep restructuring for this mechanism:

• Index selection and KV cache layout: Sparse attention relies on dynamic index selection logic to determine which tokens participate in computation. The low-level software stack must optimize this index computation process and redesign KV cache memory layout to ensure high bandwidth utilization during sparse memory access.
• Prefill and Decode compute path separation: The Prefill stage is compute-intensive and requires maximizing compute utilization; the Decode stage is memory-intensive and requires minimizing latency. Given DSA’s sparsity characteristics, different compute kernels must be customized for these two stages.
• Vision compute fusion: As a multimodal model, image feature injection must be deeply coordinated with text attention computation. The team chained MATE sparse attention operators together with KDA (Kernel Device Architecture) state management, cross-layer residuals, and vision attention to build a complete hybrid-modality execution pipeline.

2. Online Conversion Strategy for MXFP4 Expert Weights

Under the MoE architecture, storing massive expert weights in MXFP4 (4-bit floating point) can significantly reduce memory usage, but direct FP4 computation on hardware presents obstacles. The traditional approach is to pre-dequantize FP4 weights to FP8 and generate offline weight files, but this is time-consuming and multiplies memory requirements.

To address this, the team introduced an innovative “online conversion with cross-layer FP8 workspace reuse” execution strategy:

• On-demand online conversion: At the moment an expert is activated, hardware operators decompress the required FP4 weights to FP8 format in real time and feed them into the compute unit.
• Cross-layer workspace reuse: To avoid allocating a separate FP8 buffer for each MoE layer, the system uses a globally unified FP8 workspace. Since expert computation across different layers is typically serial in time, this workspace can be safely reused across layers.
• Engineering benefit: This strategy eliminates the need to generate offline FP8 weights entirely, saving substantial storage and memory overhead, and providing decisive support for rapid deployment and multi-card partitioning of the 3-trillion-parameter model.

IV. Best Practices for Heterogeneous Compute Deployment

Day-0 adaptation for ultra-large models is not just an engineering sprint but a litmus test for low-level hardware-software co-design capability. From this adaptation practice, the following general lessons can be distilled:

1. Software stack first: Unlocking hardware compute depends heavily on software stack ecosystem compatibility. Building compilers, operator libraries, and runtimes that align with mainstream ecosystems is the only path to responding to new model architecture disruptions.
2. Rebalancing compute and memory access: Facing trillion parameters and million-token contexts, blindly stacking compute power is meaningless. Sparse attention via DSA, low-bit MXFP4 quantization, and system-level dynamic memory management are the core paths to breaking through the “memory wall.”
3. Cloud-native infrastructure coordination: In real enterprise deployments, beyond the compute card adaptation itself, support from general cloud-native components is essential. For example, object storage for rapid distribution of terabyte-scale model weights, container platforms for elastic scaling of inference nodes, and logging and monitoring services for real-time tracking of long-horizon agent task execution. These general infrastructure capabilities provided by mature cloud platforms such as Baidu AI Cloud can effectively complement low-level heterogeneous compute, providing system-level assurance for large-scale application of ultra-large models.

In summary, Day-0 adaptation for a 3-trillion-parameter model marks a new stage in large model inference engineering. Through deep decoupling of the low-level software stack and fine-grained optimization of compute paths, developers can not only overcome inference barriers in multimodality and ultra-long contexts but also lay a solid engineering foundation for future larger and more complex agent models.