Day-0 Adaptation of 30-Trillion-Parameter Models on Domestic AI Accelerators: Sparse Attention and Low-Precision Weights in Practice

Day-0 Adaptation: What It Really Means

Day-0 adaptation refers to completing inference readiness on the same day a large model’s weights are publicly released. The goal is not merely “getting it to run,” but ensuring the entire pipeline works: weight loading, service process startup, and basic text and image inference requests returning valid results. For a model with 30-trillion parameters, this target requires coordinated effort across GPU memory capacity, operator coverage, communication, and scheduling.

This article is based on a Day-0 adaptation effort for a 30-trillion-parameter model supporting a 1-million-token context window. It outlines the key technical pathways for migrating the model onto domestic training-inference integrated AI accelerators and their associated software stacks. The value of such work lies in converting upstream community model code into an actual inference service on target hardware—not stopping at “it compiles and produces output.”

It is worth noting that this article focuses on generic technical challenges—migrating models with novel architectures onto domestic compute platforms—including sparse attention computation path adaptation, memory strategies for low-precision expert weights, and multi-layer software stack coordination. It does not promote any specific commercial product.

Deconstructing the Adaptation Target: Three Interdependent Paths

The broad goal of “completing adaptation” can be broken down into three interdependent chains.

Path 1: Weight Loading. Even with low-precision storage, a 30-trillion-parameter model consumes enormous weight storage. The loading process must address sharded reading, format conversion, and orchestration of data movement between host memory and accelerator memory. If the loading phase required generating a full offline low-precision copy, disk space and conversion time would become bottlenecks—so the loading strategy itself is a design problem.

Path 2: Service Startup. Starting an inference service involves computation graph construction, operator compilation, KV cache pre-allocation, and parallel strategy initialization. For models with novel attention structures, whether the compilation stage covers newly introduced operators directly determines whether the service can start successfully.

Path 3: Inference Execution. Text and image inputs follow different preprocessing and computation paths. The visual branch’s attention computation, cross-layer residual connections, and state management modules must be correctly spliced into the language backbone’s computation graph to form a complete execution path.

If any of these three paths breaks, Day-0 adaptation fails. In practice, work proceeds sequentially—first loading, then startup, then multimodal integration—with observable outputs serving as acceptance criteria at each step.

Sparse Attention: The Core Adaptation Challenge

The model employs a sparse attention structure (a common industry approach where index selection mechanisms compute attention only on selected key-value positions within long sequences). This was the most substantial computation-path change in this adaptation. With a 1-million-token context, the sequence dimension is extremely long. Dense attention would make both computation and memory consumption unacceptable; sparsification introduces significant differences from conventional attention implementations.

Adaptation work spanned three layers:

Operator Integration for Index Selection. The first step in sparse attention is selecting key-value indices for computation based on scoring or routing mechanisms. This selection process involves sorting and mask generation, which require corresponding implementations in the target hardware’s operator library. The software stack integrates sparse attention operators to execute the index selection on hardware, avoiding performance loss from falling back to generic implementations.

KV Cache Layout Adjustment. In sparse mode, the cache is no longer a simple contiguous tensor—there is an indirect mapping between indices and cache entries. The cache layout must align with index selection results; otherwise, every computation step requires a reorder operation, and overhead grows rapidly. The adaptation designed the cache organization to exploit locality inherent in sparse access patterns.

Separate Adaptation of Prefill and Decode Paths. These two phases have very different load characteristics: Prefill involves long sequences, high parallelism, and matrix-dominated computation; Decode processes only a few new tokens per step but is latency-sensitive to cache reads. Sparse attention behaves differently in each phase, so correctness and efficiency must be validated separately.

Beyond these, sparse attention operators must form a complete execution path with state management, cross-layer residual connections, and visual-branch attention computation. The difficulty lies in interface consistency between modules: state management passes and updates cache state across layers, cross-layer residuals require matching output shapes and data types across layers, and visual attention introduces additional sequences from the image branch. Any shape or precision mismatch becomes amplified as numerical errors or performance degradation in long-context scenarios.

MXFP4 Expert Weights: A Memory Strategy Without Offline Conversion

The model uses MXFP4 low-precision weights in its expert layers. This format stores weights at extremely low bit widths while maintaining numerical accuracy through block-level scaling factors, significantly compressing expert parameter memory usage. However, low-precision weights cannot directly participate in all computations—they typically need conversion to a higher-precision working format.

A straightforward approach would be pre-generating full FP8 weight copies offline and loading them at runtime. But for 30-trillion parameters, this means additional storage and conversion overhead nearly equal to the model’s size—impractical for Day-0 scenarios.

The adaptation team adopted an online conversion + cross-layer FP8 workspace reuse strategy:

• Online conversion: Weights are converted from MXFP4 to FP8 only when they are needed for computation, with results written to a workspace—avoiding full pre-generation.
• Cross-layer workspace reuse: Since layers compute sequentially, the same FP8 workspace can be reused across layers. Workspace size needs only to cover the peak demand of a single layer (or group of experts), not the entire model’s weight volume.

The key to this strategy lies in scheduling: conversion must overlap with computation pipelining, otherwise conversion latency serially adds to inference latency. Implementation requires coordinating workspace lifecycle management with compute scheduling—preparing conversion results for the next layer while the current layer is still computing. Below is illustrative pseudocode describing this pipelined relationship:

# Illustrative: pipelining online conversion with workspace reuse
workspace = alloc_fp8_workspace(max_layer_expert_bytes)
for layer in decoder_layers:
    # Asynchronously initiate MXFP4 -> FP8 conversion for this layer's expert weights
    convert_async(layer.experts, src=mxfp4_weights, dst=workspace)
    # While conversion is in-flight, execute non-expert computation in parallel
    hidden = run_attention_and_norm(hidden, layer)
    sync(workspace)
    hidden = run_expert_ffn(hidden, workspace)  # Reuse the same workspace
    # Workspace can be overwritten by the next layer once current layer completes

This approach eliminates storage and pre-conversion time at the cost of moving conversion overhead into runtime, placing higher demands on conversion kernel efficiency and scheduling overlap. In practice, edge cases must be handled: different layers may have varying expert counts or hidden dimensions, so workspace must be allocated at peak size; conversion precision and rounding must match the model definition to avoid numerical drift accumulating over long contexts.

Software Stack Coordination: From Community Code to Deployable Service

The adaptation above is not a single-point change but the result of multi-layer software stack coordination. Roles can be categorized as follows:

Layer	Responsibility	Role in This Adaptation
Inference framework layer	Inherits upstream model implementation; organizes scheduling and parallelism	Based on the framework branch for the target hardware backend, integrates model definitions
Operator library	Provides hardware-optimized computation primitives	Supplies key operators like sparse attention
Compiler toolchain	Compiles high-level operator/kernel descriptions to target hardware	Supports migration of critical computation paths for novel architectures
Kernel development language/DSL	Writes and tunes custom kernels	Used for kernel implementations via Triton, TileLang, and similar paths
Device-side compiler	Generates final device code	Handles code generation for novel architecture instructions

Concretely, the migration flow involved building on the inference framework branch that inherits the upstream model implementation, then using the operator library, Triton and TileLang support for the target hardware, and the device-side compiler to migrate and integrate the new architecture’s critical computation paths. The essence of this process: taking computation logic described in generic implementations from the community, mapping it segment by segment to operators and kernels that execute efficiently on target hardware, and assembling the mapped results back into a complete model execution graph—ultimately turning it into an actual inference service on the AI accelerator.

Two aspects of this flow are easily underestimated. First, operator coverage assessment: you must inventory all operators used by the model and classify them into three categories—those with existing optimized implementations on target hardware, those needing new implementations, and those that can fall back to generic versions—then prioritize hot-path operators affecting latency. Second, numerical consistency verification: starting from an FP32 reference implementation, compare output deviations layer by layer across the low-precision path to ensure that sparse index selection and low-precision expert computation do not introduce errors beyond acceptable tolerance.

Acceptance Criteria and Future Optimization Directions

Day-0 adaptation acceptance is typically functional: weights load successfully, service starts normally, and basic text and image inference requests return reasonable results. This is just the starting point. Subsequent optimization generally proceeds along several directions:

• Long-context performance: At 1-million-token context, Prefill latency and peak memory are primary metrics; the overhead of sparse attention index selection requires focused measurement.
• Decode throughput: Batch size and KV cache layout jointly determine decoding throughput; cache hit locality under sparse access patterns warrants further exploration.
• Low-precision path stability: Runtime overhead and numerical drift introduced by online conversion require long-term monitoring, especially under varying input lengths and batch size combinations.
• Multimodal path completeness: Beyond basic inference, the visual branch and language backbone integration should cover more complex input combinations.

From a broader perspective, this adaptation effort validates an engineering pathway: when faced with models using novel attention structures and extremely low-precision weights, domestic training-inference integrated compute platforms combined with a complete software stack can convert model code into a deployable service on the day weights are released. The reusable components of this pathway—operator coverage assessment methodology, online conversion with workspace reuse for low-precision weights, and phased acceptance processes—offer direct reference value for future migrations of similar model architectures.