Technical Path for Adapting Ultra-Large-Scale Models to Domestic AI Accelerators

1. Background: Challenges in Adapting Ultra-Large-Scale Models

In recent years, model parameter counts have continued to climb, driving nonlinear growth in compute demand for both training and inference. When a model reaches trillion-scale parameters, the question is no longer “can it run” but “how to run it stably and efficiently within limited memory, limited interconnect bandwidth, and a finite engineering timeline.” Meanwhile, million-token context lengths further amplify KV Cache memory consumption, significantly increasing the complexity of resource scheduling during inference.

Under these conditions, AI accelerators and their accompanying software stacks must complete model adaptation on Day-0—the day the model is open-sourced. This places stringent demands on operator coverage, compiler optimization capability, and engineering responsiveness. This article uses a real-world adaptation of a trillion-parameter, million-context model on a domestic training-inference integrated accelerator with a proprietary software stack as the reference thread, outlining a general technical path for adapting ultra-large models that can serve as a reference for developers working on inference deployment and heterogeneous compute adaptation.

Note that this article focuses on technical methodology and engineering practice. It does not cover specific commercial partnership details, and the described workflow represents a general paradigm applicable to similar adaptation efforts.

2. Model Side: Understanding Architecture and Compute Characteristics First

The first step in adaptation is not writing code—it is model analysis. For ultra-large-parameter models, at least the following characteristics need to be clarified:

1. Parameter Organization and Sparse Activation

Trillion-parameter models commonly adopt sparse Mixture-of-Experts (MoE) structures, where the total parameter count is enormous but only a subset of experts is activated during any single forward pass. This means memory capacity requirements are driven by total parameters, while compute is driven by activated parameters. The adaptation must accommodate all expert weights in the memory layout while ensuring the correctness and low latency of routing logic on the compute path. Routing branches typically involve sorting, Top-K selection, and scatter-write operations. These operators are mature in the general GPU ecosystem but often require reimplementation or equivalent substitution on heterogeneous software stacks.

2. Attention Architecture and Long Context

Million-token contexts directly inflate both attention computation complexity and KV Cache size. With standard full attention, the KV Cache rapidly consumes available memory. Therefore, it is critical to check whether the model employs Grouped Query Attention (GQA), sliding window attention, chunked prefill, or similar mechanisms, and to design cache paging and eviction policies accordingly. In practice, KV Cache is typically managed in fixed-size blocks, combined with prefix caching to reduce redundant computation.

3. Parallel Sharding Strategy

Inference for ultra-large models generally requires multi-card parallelism. Tensor parallelism (TP) shards attention head dimensions and expert weights, pipeline parallelism (PP) shards network layers, and expert parallelism (EP) shards MoE experts. Each sharding method imposes different communication requirements: TP requires frequent AllReduce, while EP requires All-to-All or scatter-gather patterns. The adaptation must verify that the software stack’s collective communication library covers these primitives and assess bandwidth behavior under small-scale interconnect topologies.

4. Precision and Quantization

Ultra-large-model inference commonly uses BF16/FP16 mixed precision, with some scenarios introducing FP8 or weight-only quantization to reduce memory bandwidth pressure. Before adaptation, it is necessary to confirm the numerical format support matrix of the accelerator to avoid fallback to inefficient operator implementations.

3. Software Stack Side: Migrating the Inference Critical Path

After model analysis, adaptation shifts to migrating the inference critical path. The “critical path” refers to the main compute chain from input embedding to output logits, typically comprising five module types: linear layers, normalization, attention, activation functions, routing, and communication. A reusable migration workflow is roughly as follows:

Step 1: Build an Operator Inventory and Coverage Matrix

Traverse the model computation graph layer by layer to produce an operator inventory, compare it against the operators already supported by the software stack, and mark each operator as “directly supported,” “needs graph fusion substitution,” or “needs custom implementation.” This step determines the bulk of subsequent workload.

Step 2: Graph-Level Optimization and Fusion

For adjacent small operators—such as LayerNorm + residual, RoPE rotary position embedding, and SiLU gating—prioritize graph fusion to merge them into composite operators, reducing kernel launch overhead. In long-context scenarios, fusing the QKV projection with RoPE before attention often yields substantial end-to-end gains.

Step 3: Custom Operator Implementation

For operators not yet supported by the software stack, implement mathematically equivalent versions. Taking MoE routing as an example, it can be decomposed into four steps: scoring, Top-K selection, weight normalization, and scatter-gather aggregation. The aggregation step can be implemented using indexed gather/scatter primitives:

# Pseudocode: equivalent implementation approach for expert routing
scores = gate(x)                      # [tokens, num_experts]
topk_w, topk_idx = topk(scores, k)    # select activated experts
topk_w = normalize(topk_w)            # normalize weights
# Compute per expert in batches, then scatter-write back by original index
for e in range(num_experts):
    mask = (topk_idx == e)
    if mask.any():
        out[mask] += expert[e](x[mask]) * topk_w[mask]

The key to this implementation is ensuring batch-processing efficiency in grouped computation, avoiding per-token loops that introduce scheduling overhead.

Step 4: Memory and Cache Management

For million-token contexts, paged KV Cache and dynamic batch scheduling are required. In practice, cache is allocated in fixed token blocks, and blocks are reclaimed after request completion rather than releasing the entire allocation, enabling mixed-batch inference over variable-length sequences.

Step 5: Precision Alignment and Numerical Validation

After migration, outputs of key intermediate layers must be compared under identical inputs to confirm numerical errors remain within tolerance. Common issues include floating-point deviations caused by different reduction orders and altered rounding boundaries introduced by fused operators.

4. Validation: From Single Run to Stable Operation

The goal of Day-0 adaptation is “stable operation,” not merely “one successful run.” A three-tier validation system is recommended:

• Functional tier: Compare outputs layer by layer on small-scale inputs to confirm operator semantic correctness;
• Performance tier: Measure time-to-first-token latency and throughput at fixed batch sizes and sequence lengths, establishing reproducible baselines while observing peak memory usage under long sequences;
• Stability tier: Run sustained stress tests for several hours, monitoring memory fragmentation, communication timeouts, and kernel anomalies to confirm no degradation over extended operation.

Performance baselines should record four metadata items: hardware configuration, software stack version, parallelism strategy, and input distribution. Without these, reproducibility is impossible.

5. Engineering Recommendations and Summary

Based on the practices described above, several transferable lessons emerge:

1. Analyze before coding. Model architecture and sparsity patterns determine optimization priorities; blindly applying dense-model tuning experience often yields little benefit.
2. Favor graph fusion; write custom operators sparingly. Custom operators carry high maintenance costs. If fusion solves the problem, avoid adding new kernels.
3. Memory management is the deciding factor for long contexts. Paged caching and prefix reuse should be designed as first-class citizens.
4. Establish baselines first. Solidify a reproducible performance baseline before optimization; otherwise, it is impossible to judge whether an optimization is effective.
5. Watch numerical consistency. Precision drift in heterogeneous migration is the most insidious issue. Build comparison tooling early in the workflow.

Day-0 adaptation of trillion-parameter models is, at its core, a systems engineering challenge centered on operator coverage, memory efficiency, and communication overhead. By completing the migration and validation of the critical inference path, domestic training-inference integrated accelerators and their software stacks demonstrate feasibility for ultra-large-model inference. For developers, mastering this “analyze–migrate–validate” methodology holds more long-term value than memorizing any specific hardware model.