Adapting Trillion-Parameter Open-Source LLMs to Chinese Domestic AI Accelerators: A Technical Deep Dive

1. Background: When Oversized Open Models Meet Domestic Compute

Over the past two years, the parameter counts of open-source large models have climbed rapidly along a curve of “tens of billions → hundreds of billions → trillions.” Recently, the open-source community released a 3-trillion-parameter model that supports million-token contexts. According to public information, after large-scale post-training and reinforcement learning, this model shows across-the-board improvements in code generation, complex reasoning, long-horizon agentic tasks, and domain-specific knowledge QA, and its performance on public code and agent benchmarks is now close to top-tier closed-source models.

At the same time, China’s domestic AI compute is maturing quickly. A new generation of training-and-inference integrated accelerator cards has been delivered, and the accompanying self-developed unified compute software stack is steadily improving, covering the full chain from upper-layer deep learning framework interfaces down to hardware kernels. Against this backdrop, “Day-0” adaptation—the ability to run a newly open-sourced model stably on domestic hardware on its release day—has become a useful window into the maturity of this compute ecosystem.

It is worth emphasizing that Day-0 adaptation is far more than “getting a demo to run.” At minimum, it means three things: every critical inference path works end to end, numerical results align with the reference implementation, and performance reaches a usable level. What this really tests is the software stack’s abstraction capability, operator coverage, and level of engineering automation.

2. Why Adapting Trillion-Scale Models to New Hardware Is Hard

2.1 The memory wall: do the math first

The first-order problem in adapting an oversized model is not “computing fast” but “fitting it in memory and moving it around.” Take 3 trillion parameters:

• Stored in FP8 precision, the weights occupy roughly 3 TB;
• Stored in BF16 precision, roughly 6 TB;
• A single accelerator card typically offers tens to a hundred-plus GB of memory, so the weights alone must be spread across dozens of cards—before counting activations and the KV cache.

KV cache memory can be estimated with the following formula:

KV cache memory ≈ 2 × number of layers × number of KV heads × head dimension
                 × sequence length × batch size × bytes per element

For a model with 100 layers, 80 KV heads, and a head dimension of 128, storing the KV cache in 8-bit precision requires about 2 × 100 × 80 × 128 × 1B ≈ 2 MB per token. A single request with a million-token context would therefore need roughly 2 TB of KV cache alone. Long-context inference must rely on multi-node distribution and fine-grained cache management—any naive implementation will exhaust memory instantly.

2.2 Architectural complexity: the dual challenge of MoE and ultra-long context

Modern oversized models generally adopt a Mixture-of-Experts (MoE) architecture: the model contains a large number of expert networks, and each token is dynamically assigned by a router to a small subset of experts. This creates two engineering problems. First, expert weights must be distributed across devices, and the routing process introduces All-to-All communication. Second, expert workloads are naturally imbalanced, so some devices run hot while others sit idle.

Ultra-long context adds pressure from another dimension: attention computation cost grows with sequence length, the KV cache expands linearly, and positional encoding must support extrapolation. In addition, models trained with large-scale reinforcement learning tend to generate long chains of thought and make frequent multi-turn tool calls at inference time, significantly increasing per-request output length and session duration—placing higher demands on the scheduling stability of the inference framework.

2.3 Software stack and operator ecosystem maturity

Official implementations of open-source models are typically written on top of mainstream deep learning ecosystems, where many operators, communication primitives, and memory management assumptions are designed around established hardware ecosystems. For new hardware to take on these models, it must answer three questions: Do the operators exist (functional coverage)? Are they fast (performance coverage)? Is the toolchain smooth (migration cost)? A single slow operator on the critical path can drop overall throughput by an order of magnitude.

3. The Adaptation Path: From Weights to Inference

3.1 Model parsing and weight conversion

The first step is converting the original weights into a format friendly to the target hardware. The core work includes shard re-arrangement, pre-splitting according to the parallel strategy, memory layout alignment, and per-layer precision policies. Representative pseudocode:

# Pseudocode: converting an oversized model checkpoint for target hardware
def convert_checkpoint(src_ckpt, dst_dir, tp_size=8, pp_size=4):
    state_dict = load_sharded_checkpoint(src_ckpt)
    for name, tensor in state_dict.items():
        # 1) Pre-split by parallel strategy: attention heads by tensor parallelism,
        #    expert weights by expert parallelism
        shards = pre_split(name, tensor, tp_size=tp_size, pp_size=pp_size)
        for rank, shard in enumerate(shards):
            # 2) Memory layout alignment: meet the target hardware's requirements
            #    for memory access continuity and alignment
            shard = repack_layout(shard, target="aligned_layout")
            # 3) Per-layer precision policy: numerically sensitive layers stay in
            #    high precision; the rest are cast per policy
            shard = cast_precision(shard, policy=layerwise_policy(name))
            save_shard(dst_dir, name, rank, shard)

The per-layer precision policy is especially critical: not every layer is suitable for reduced precision. Embedding layers, normalization layers, and router-related computations usually need to stay at higher precision; otherwise, errors amplify as they stack across deep layers.

3.2 Operator mapping and migrating the critical inference path

The critical path of LLM inference can be summarized as: tokenization and embedding → N decoder blocks (attention + feed-forward/expert networks) → output sampling. During adaptation, operators are typically handled with a three-tier strategy:

# Pseudocode: tiered migration strategy for critical operators
class AttentionOp:
    def dispatch(self, hw):
        if hw.supports("fused_attn_kernel_v3"):
            return FusedKernel()        # First choice: deeply fused kernel, best performance
        if hw.supports("fused_attn_kernel_v2"):
            return CompatibleKernel()   # Second: mature compatible kernel, guarantees availability
        return DecomposedFallback()     # Fallback: decomposed standard operators, guarantees function

This “fusion first, compatibility as a transition, decomposition as a fallback” tiered strategy is a hallmark of software stack engineering maturity: it keeps the model running stably even before the optimal kernels cover every scenario, while leaving room for later performance iteration.

3.3 Distributed parallelism: spreading 3 trillion parameters across devices

Faced with TB-scale weights, real deployments use a hybrid combination of parallelism schemes:

• Tensor parallelism (TP): splits a single layer’s weights and activations across multiple cards; communication is dominated by AllReduce and AllGather;
• Pipeline parallelism (PP): splits by network depth, with micro-batch scheduling to reduce pipeline bubbles;
• Expert parallelism (EP): distributes different experts across devices; the token dispatch produced by routing relies on All-to-All communication and must be paired with load-balancing mechanisms;
• Data parallelism (DP): replicates complete serving instances to scale throughput.

This is where a unified training-inference architecture pays off: the parallel splits and communication optimizations validated during training can be reused on the inference side, significantly shortening the adaptation cycle.

3.4 Memory and scheduling optimizations for long context

For million-token contexts, the industry-standard optimization stack includes:

1. Paged KV cache management: allocates memory at page granularity, eliminating fragmentation and improving memory utilization;
2. Prefix caching: system prompts and shared prefixes across multi-turn conversations are computed once and reused across requests;
3. KV quantization: stores the KV cache at lower precision; the capacity-for-quality trade-off must be validated through evaluation;
4. Context parallelism: splits ultra-long sequences across multiple cards for attention computation;
5. Chunked prefill scheduling: breaks the prefill of long inputs into multiple chunks, preventing a single request from monopolizing compute for extended periods.

3.5 Precision alignment and stability verification

Numerical consistency is where cross-hardware adaptation most often breaks. Common practice is to compare intermediate activations layer by layer against the reference implementation and monitor the KL divergence and cosine similarity of output distributions. When deviation is detected, layer-by-layer error injection is used to localize the specific operator at fault. Long-duration concurrent stress testing is also required to observe whether memory usage curves remain stable, and whether numerical overflow or silent failures occur.

4. Evaluation: What Counts as a Successful Adaptation

Whether an adaptation has succeeded must be answered by two classes of metrics together:

• Capability metrics: on public benchmarks for code generation, long-horizon agent tasks, and knowledge QA, the ported model’s scores should be essentially consistent with the reference implementation—in other words, “migration must not lose capability”;
• Engineering metrics: throughput, first-token latency, P99 end-to-end latency, memory utilization, and compute utilization (MFU).

The advice for developers and enterprise users: do not rely solely on the generic benchmarks published by vendors. Build your own evaluation sets based on real business workloads and complete end-to-end validation on the target hardware. In phases where local heterogeneous hardware is unavailable, you can first use cloud-hosted model services (for example, the LLM services offered by Baidu AI Cloud) for functional verification and result comparison, then return to your self-built environment for performance tuning.

5. Implications for the Industry and Developers

1. The software stack determines hardware value. Peak compute is not the same as usable compute; operator coverage, fusion capability, and communication efficiency are what determine real returns. Day-0 adaptation is precisely the touchstone of software stack maturity.
2. Migration cost is a core selection variable. The compatibility layer determines how high the migration threshold is, while native optimization on the critical path determines the performance ceiling—you need both.
3. When evaluating diverse compute, look at three things: whether mainstream open-source models are adapted promptly, whether the migration toolchain is complete, and whether performance data under real workloads is transparent.
4. Ecosystem signals matter. When “runs on release day” goes from being news to being the norm, it marks the shift of the domestic compute ecosystem from “can it run” to “does it run well”—which materially lowers the barrier for enterprise trials and fuels the growth of upper-layer applications.

6. Conclusion

Day-0 adaptation of a 3-trillion-parameter model on training-inference integrated domestic accelerators may look like a simple version-alignment event, but at its core it is a concentrated test of an entire engineering system: weight conversion, operator migration, parallel scheduling, long-context optimization, and precision verification. For developers, understanding this path not only helps in evaluating and selecting diverse compute, but also provides a methodology to borrow when building your own inference services: first do the math on memory and communication, then design tiered operator and parallel strategies, and finally close the loop with strict numerical alignment and benchmark evaluation. The depth of software-hardware co-design ultimately determines how far a large model travels from “it runs” to “it works well.”