Adapting Trillion-Parameter LLMs on Domestic GPUs: Inference Pipeline Analysis and Best Practices

I. Background: Why Ultra-Large Model Inference Adaptation Matters

As the parameter scale of large pre-trained models continues to grow, their capabilities expand, but so do the engineering challenges on the inference side. When a model reaches the trillion or even tens-of-trillions parameter scale, simply “getting it to run” is no easy task. At this scale, computing hardware, programming models, inference frameworks, and model structures must achieve a high degree of synergy. Any mismatch in this chain can lead to loading failures, out-of-memory (OOM) errors, or severe performance degradation.

In this context, building ultra-large model inference capabilities around domestic full-function GPUs offers dual value. First, it validates the speed and completeness with which a heterogeneous computing stack can support cutting-edge model architectures. Second, it provides a reusable engineering path for subsequently deploying ultra-large models in a controllable, autonomous hardware environment.

The “Day-0 adaptation” of an ultra-large model typically refers to completing the basic closed loop—from model acquisition, structure parsing, and weight mapping to operator adaptation and service launch—immediately following the model’s open-source release. This goal sounds straightforward, but when a model reaches the 3-trillion parameter scale, every step magnifies the importance of engineering details.

II. Breaking Down the Adaptation Goals: From Model Structure to Available Service

A complete adaptation process for ultra-large model inference should focus not just on “whether it can produce a result” but on whether the adaptation chain is reproducible and scalable. From a general engineering perspective, the entire chain can be broken down into four key phases.

1. Model Structure Parsing and Weight Mapping

Ultra-large models typically employ highly parallelized structural designs, such as Mixture-of-Experts (MoE), Multi-Head Latent Attention (MLA), and separated multimodal encoders and decoders. The first step in adaptation is fully parsing the model structure definition and mapping the weight tensors to a format recognizable by the target hardware.

The core challenge here is that weight sharding methods, tensor naming conventions, precision types, and offsets may differ across training frameworks. The adaptation team must establish a universal weight parsing workflow to convert original weights into a tensor layout consistent with the inference engine, while verifying the shape and numerical range of each layer’s parameters to prevent hidden errors during loading.

2. Memory Planning and Distributed Sharding

Even with low-precision representation, a 3-trillion parameter model requires thousands of gigabytes of storage for weights alone. A single card obviously cannot handle this, necessitating multi-card distributed inference. Common sharding strategies include Tensor Parallelism (TP), Pipeline Parallelism (PP), and Expert Parallelism (EP).

The core goal of memory planning is to ensure that the resident data volume on each accelerator card does not exceed the physical memory limit, while minimizing inter-card communication. For MoE structures, the placement strategy for expert weights must also be considered: should they all reside in memory, or be dynamically swapped in and out based on activation? Different choices directly impact Time-To-First-Token (TTFT) and throughput.

A practical approach is to first calculate the minimum number of cards based on the total parameter size and per-card memory capacity, then select sharding dimensions based on the model structure. For example, attention layers are suitable for TP, feed-forward layers for EP, and PP can be used between layers to distribute the memory pressure of storing activations.

3. Operator Adaptation and Computation Graph Optimization

The model structure may contain numerous non-standard operators, particularly in modules involving sparse activation, Rotary Position Embedding (RoPE), and multimodal feature alignment. The goal of operator adaptation is to ensure these operators execute efficiently on the target hardware, rather than barely running through a generic fallback path.

During adaptation, basic operator combinations are typically used first to verify logical correctness, followed by gradual replacement with hardware-friendly, high-performance implementations. For fusable operator sequences in the computation graph—such as normalization and linear layers, or activation functions and element-wise operations—fusion optimization can be applied to reduce memory read/write operations for intermediate results.

4. Service Encapsulation and Multimodal Request Handling

The inference service must support concurrent requests, dynamic batching, streaming output, and ultra-long context management. For models supporting text and image inputs, multimodal data encoding and sequence concatenation must be completed at the request entry point.

Key points for service encapsulation include: the input side must support shunted processing for different modalities; the output side must maintain streaming stability; and the scheduling layer must dynamically adjust batch strategies based on request length and current load. For 1-million-token context lengths, a rational caching mechanism must be designed to avoid recomputing Key-Value (KV) pairs for historical sequences.

III. Collaboration Methods for Heterogeneous Computing Stacks

Adapting to domestic full-function GPUs essentially involves rebuilding support for mainstream model structures on a relatively independent programming model and software stack. This means the adaptation team must focus on collaboration across three layers simultaneously.

1. Hardware Layer: Memory, Interconnect, and Compute Units

Ultra-large models place extremely high demands on memory bandwidth and inter-card interconnect bandwidth. During adaptation, it is necessary to evaluate the memory capacity, memory bandwidth, and inter-card communication topology of each accelerator card. If inter-card interconnect bandwidth is insufficient, large-scale TP will create severe communication bottlenecks, drastically reducing inference throughput.

Therefore, the sharding strategy must match the hardware topology. For instance, prioritize TP within card groups with high interconnect bandwidth, and use PP between card groups with lower bandwidth, keeping communication costs within an acceptable range.

2. Software Stack Layer: Drivers, Compilers, and Runtime

The software stack must provide underlying device management, memory allocation, stream execution, and synchronization primitives. The upper-layer inference framework relies on these foundational capabilities to execute computation graph scheduling and memory reuse.

For new model structures, the compiler must support dynamic shapes, sparse computation, and custom operators. The runtime should possess streaming load capabilities, allowing model weights to be read in batches from storage media rather than being loaded entirely into host memory at once.

3. Framework Layer: Loading, Scheduling, and Service Interfaces

The inference framework plays a pivotal role in adaptation. It must understand the model structure definition, call the software stack to execute operators, and expose standardized service interfaces upwards. A viable framework solution typically needs to support the following capabilities:

• Distributed weight loading and verification
• Multi-card collaborative inference scheduling
• Dynamic request queuing and batching
• Streaming output and timeout control
• Concise HTTP or RPC interfaces

The combination of these capabilities determines whether the adaptation can move from “working in the lab” to “deployable as a service.”

IV. Additional Challenges of Long Context and Multimodal Inference

The model involved in this adaptation supports a 1-million-token context and dual input modalities (text and images), introducing extra complexity to the inference system.

1. Caching and Scheduling for Long Contexts

A million-level context means the KV cache for a single request can reach tens of gigabytes. If each concurrent request allocates its cache independently, memory will be exhausted rapidly. Therefore, the service layer must introduce a cache reuse mechanism.

A common practice is using prefix caching: when multiple requests share the same prefix content, only one cache is computed and stored, and subsequent requests reuse that portion. Another approach is hierarchical caching, storing the cache of historical sequences in blocks and reading them on demand based on the current generation position, thereby reducing resident memory usage.

2. Efficient Encoding of Multimodal Inputs

Text and image inputs must be converted into a unified latent space representation through different encoding paths before sequence concatenation. For high-resolution images, directly flattening them into a sequence causes length explosion. Therefore, block-wise encoding or convolutional downsampling is typically used to compress images into compact sequences of visual tokens.

In the inference service, the scheduling of multimodal requests also requires special handling. The image encoding part is compute-intensive, while the text generation part is memory-intensive. Mixing them easily leads to unbalanced loads. By separating visual encoding and text generation into different stages of batch tasks, overall throughput can be improved.

V. Schematic Implementation of the End-to-End Inference Pipeline

The following pseudo-code illustrates a simplified multi-card inference service loading and execution flow, helping to clarify the calling relationships between different phases.

# 1. Model weight loading and distributed sharding
checkpoint = load_checkpoint_from_storage(model_path)
sharded_weights = shard_by_strategy(
    checkpoint,
    strategy={
        "attention": "tensor_parallel",
        "expert": "expert_parallel",
        "layer": "pipeline_parallel"
    },
    num_devices=cluster_device_count
)
# 2. Initialize inference engine
engine = InferenceEngine(
    model_config=model_config,
    devices=available_devices,
    weights=sharded_weights
)
engine.initialize()
# 3. Start inference server
server = InferenceServer(engine)
server.register_route("/v1/generate", handle_generate)
server.register_route("/v1/chat", handle_chat)
server.start(host="0.0.0.0", port=8080)
def handle_chat(request):
    # Multimodal routing
    text_tokens = tokenizer.encode(request.prompt)
    image_tokens = []
    if request.image is not None:
        image_tokens = vision_encoder.encode(request.image)
    input_ids = image_tokens + text_tokens
    # Long context cache lookup
    cache_key = compute_prefix_hash(input_ids[:-1])
    kv_cache = cache_manager.get(cache_key)
    # Stream generation
    stream = engine.generate_stream(
        input_ids=input_ids,
        kv_cache=kv_cache,
        max_new_tokens=request.max_tokens
    )
    for token in stream:
        cache_manager.update(cache_key, token)
        yield token

The above flow is for logical illustration only; actual systems also require modules for precision management, exception recovery, and dynamic scaling. However, it clearly demonstrates the complete pipeline from weight loading to multimodal request handling.

VI. Evaluation Dimensions for Adaptation Results

After completing the Day-0 adaptation, how do we determine if the quality meets the standard? Generally, this can be evaluated from four dimensions.

Functional Completeness: Can the model load correctly, can the service start normally, and can text and image inputs yield reasonable outputs?

Stability: Under sustained concurrent requests, does the service experience memory leaks, response timeouts, or random failures?

Performance Baseline: Under single-card or multi-card configurations, are the TTFT and generation throughput within a reasonable range, providing a baseline for subsequent optimization?

Transferability: Can the toolchain and processes formed during adaptation be reused on other ultra-large models with similar structures, thereby reducing future adaptation costs?

For models with over a trillion parameters, getting it to run is just the starting point. A truly valuable adaptation forms a continuously iterable engineering methodology based on functional completeness.

VII. Implications for Future Inference Platform Construction

Ultra-large model inference is shifting from “can it run” to “can it run efficiently, stably, and cost-effectively.” This adaptation practice yields at least three key insights.

First, inference platforms need to incorporate hardware topology awareness into their core design. Differences in inter-card interconnect bandwidth, memory capacity, and computing power directly dictate sharding and scheduling strategies. The platform should provide automated topology detection and strategy recommendation capabilities.

Second, long-context services require an independent cache management subsystem. As context lengths continue to grow, cache design will directly impact service costs. Decoupling cache management from the inference engine facilitates independent optimization and scaling.

Third, the scheduling granularity for multimodal inference needs to be finer. Visual encoding and text generation have different resource consumption characteristics. Mixed scheduling requires dynamic adjustments based on real-time load, rather than relying on fixed batch strategies.

The continuous investment in adapting ultra-large models to domestic computing power is gradually narrowing the time gap between model release and service availability. The accumulation of this capability is not only significant for infrastructure autonomy and control but also provides developers and enterprises with a practical choice for deploying advanced models in diverse computing environments.