Day-0 Adaptation of a 3-Trillion Parameter LLM on Domestic GPUs: From Migration to Optimization

A Practice in Adapting Ultra-Large Models on Domestic Hardware

In early November 2024, an open-source large language model (LLM) with a 3-trillion parameter scale was officially released. Its capabilities cover complex scenarios such as code generation, agent tasks, financial research, and long-document understanding, supporting a context window of up to 1 million tokens. Shortly after the model’s release, a domestic GPU vendor announced the completion of Day-0 adaptation for the model based on its integrated AI training and inference acceleration cards and self-developed software stack. Day-0 adaptation means that the inference environment was made available on the exact day the model was open-sourced.

The significance of this event goes far beyond simply “getting it to run.” A 3-trillion parameter model implies that the weight files alone require several terabytes of storage, far exceeding the VRAM capacity of a single GPU. Furthermore, a 1-million token context imposes stringent requirements on KV Cache storage and memory access bandwidth. Without deep customization support from the official framework, achieving Day-0 adaptation on a domestic GPU platform relies heavily on system-level hardware-software co-design and engineering optimization.

Based on this technical milestone, this article breaks down the complete adaptation workflow—from model analysis and inference path migration to resource scheduling and performance tuning. It also explores general strategies for domestic AI computing platforms when dealing with ultra-large models.

1. Understanding the Target Model’s Resource Profile

The first step in any adaptation work is conducting an accurate resource profile analysis of the model itself. For an LLM of this scale, such as Kimi K3.1 Pro, the core characteristics can be categorized into three dimensions: capacity, context, and operator形态 (operator patterns).

Regarding capacity, even stored in low precision (such as FP8/BF16 mixed precision), a 3-trillion parameter model requires 3 to 6 TB of storage space. This dictates that inference tasks must adopt a multi-GPU parallel strategy, as a single card cannot independently host the complete model. Regarding context, supporting 1 million tokens means that the VRAM occupied by the KV Cache during inference may exceed the weights themselves. Estimating based on common multi-head attention mechanisms, the KV Cache for a million-level sequence in BF16 precision could occupy several terabytes or more. Regarding operator patterns, such models typically employ sparse attention (e.g., a mix of sliding window and global attention), Mixture of Experts (MoE) architectures, or dense attention variants. Different patterns consume computing units and memory bandwidth in vastly different ways.

Therefore, the adaptation team’s primary tasks are: to parse the tensor shapes, operator types, parallel dimensions, and numerous configuration parameters of each layer in the model’s configuration file; and then to establish a “resource-demand” mapping table against the target hardware’s VRAM capacity, bandwidth, and compute peaks. Only by mapping the model’s “soft” computation graph onto the hardware’s “hard” physical resources can a reasonable partitioning and scheduling strategy be formulated.

2. Inference Path Migration: From Computation Graph to Execution Engine

The core barrier to Day-0 adaptation lies in the migration of the inference path. Current mainstream LLM frameworks (such as Python-based inference engines) often rely on specific acceleration libraries from a particular GPU vendor (e.g., platform-specific operator and communication libraries). When the target hardware switches to a domestic GPU, every operator in the computation graph must find a corresponding implementation or a viable fusion alternative within the domestic software stack.

This process typically involves three steps:

1. Operator Mapping and Replacement: Systematically analyze the operator categories within the model’s computation graph, including matrix multiplication, normalization, activation functions, attention computation, and RoPE positional encoding. For standard operators, directly invoke the high-performance implementations from the domestic software stack. For custom operators (such as certain attention variants), manual implementation using low-level programming interfaces is required.

2. Memory Allocation and KV Cache Management: In ultra-large context scenarios, the KV Cache allocation strategy directly impacts inference throughput. The adaptation process requires implementing a dynamic memory pool that elastically allocates VRAM based on the actual generated sequence length, rather than statically reserving the maximum value, thereby reducing memory fragmentation. Additionally, for long-sequence attention computation, the KV Cache needs to be stored in chunks within the HBM, and asynchronous prefetching should be used to reduce memory access latency.

3. Communication Primitive Alignment: Multi-GPU parallel inference relies heavily on cluster communication (e.g., AllReduce, AllGather). The domestic platform must provide interfaces compatible with mainstream communication libraries and optimize collective communication based on its own network topology (e.g., ring, tree, or Torus structures) to prevent communication bottlenecks from slowing down overall inference speed.

In this adaptation practice, the key inference path migration completed by the team using their software stack is essentially the execution of the above process. Since the model is open-source, the team had access to accurate network structure description files, making standardized operator mapping possible.

3. Memory Optimization Strategies for Multi-GPU Distributed Inference

To address the impossibility of loading a 3-trillion parameter model onto a single GPU, a hybrid strategy of multi-GPU pipeline parallelism and tensor parallelism must be employed. A sound deployment plan is: to partition the model into several Stages by layer, with each Stage deployed across multiple GPUs; within a Stage, tensor partitioning is applied to the dimensions of the attention heads to reduce the VRAM pressure on a single card.

In actual deployment, memory optimization focuses on three levels:

• Weight Storage Compression: Utilizing low-bit quantization (such as FP8 or INT8) is the most direct method to reduce weight volume. However, quantization must carefully handle sensitive layers (like the final normalization layer) to avoid precision drift. For MoE models, since each token only activates a subset of experts, sparse storage can be used—keeping only the weights of activated experts in VRAM—significantly lowering peak memory requirements.

• Activation Recomputation: During the inference phase, not all intermediate activation values from the forward pass need to be retained. By employing a Recompute strategy, intermediate results are recalculated during backward passes (or subsequent generation steps), trading a small amount of compute overhead for substantial memory savings. For a 3-trillion parameter model, this strategy can typically save over 30% of VRAM usage.

• KV Cache Quantization and Tiered Offloading: In long-context scenarios, the primary memory bottleneck is the KV Cache. One effective method is to apply INT8 quantization to the KV Cache, paired with per-channel scaling factors to maintain precision. Another strategy is a “VRAM-to-Host Memory” tiered scheduling approach: offloading the KV Cache of infrequently used layers to CPU memory and asynchronously moving it back when needed. This heterogeneous caching mechanism allows a single-node system to handle ultra-long sequences that would otherwise require multi-node setups.

4. Attention Computation Optimization for Long Contexts

Supporting a 1-million token context means that the complexity of attention computation transforms from a standard O(n²) problem into a highly challenging engineering task. Even with IO-aware attention optimization algorithms like FlashAttention, sequence lengths reaching the million level still require additional algorithm-level optimizations.

In practice, a PagedKV Cache mechanism is necessary. This maps continuous KV blocks to non-contiguous physical VRAM pages, allocating them on demand. Similar to an operating system’s virtual memory, this mechanism effectively prevents memory fragmentation caused by long sequences while supporting KV sharing across multiple sequences, reducing redundant prefix computations.

For sparse attention structures, it is possible to identify local attention windows and global anchor tokens within the model. During computation, attention scores are calculated only for tokens within the window and the anchor tokens, skipping full global computation. This not only reduces the computational load but also minimizes the read/write bandwidth of the KV Cache. For the decoding phase of long sequences, the inference process must be decomposed into two stages: Prefill and Decode, each utilizing different computational scheduling. The Prefill phase is compute-intensive and suitable for large-batch parallelism; the Decode phase is memory-access intensive, requiring the minimization of random KV Cache reads during the generation of each token.

5. Software Stack Compatibility and Automated Toolchain

Beyond low-level operator and memory optimizations, a mature adaptation process relies heavily on the automation level of the software stack. The integrated toolchain provided by the domestic GPU platform’s accompanying software stack covers everything from model import to compiled execution. However, the smooth completion of Day-0 adaptation is largely attributed to accumulated capabilities in the following areas:

• Seamless Frontend Framework Integration: The platform must support intermediate representations exported by mainstream deep learning frameworks, allowing models to be loaded without users modifying their scripts. This means the platform’s frontend parser needs to be compatible with various operator dialects and capable of automatically performing graph optimizations (e.g., operator fusion, constant folding).

• Auto-Tuning Engine: For matrix multiplications, convolutions, or attention computations of different shapes, the engine automatically searches for the optimal partitioning strategies (e.g., Tile size, vectorization width, loop unrolling factors). This hardware Cost Model-driven search typically yields a 20% to 50% performance improvement over manually written operators.

• End-to-End Profiling Tools: Adaptation is not a one-time effort; it requires continuous iterative optimization. Profiling tools should be able to display fine-grained metrics such as the execution time of each operator, VRAM bandwidth utilization, and communication wait proportions, helping developers quickly pinpoint performance bottlenecks during model iterations.

It is worth mentioning that the platform emphasized the role of the “MUSA software stack” during adaptation, which is essentially a unified heterogeneous computing programming model and runtime. This unified architectural design significantly reduces the complexity of cross-platform migration—writing an operator once allows it to be reused across both training and inference scenarios, facilitating rapid adaptation for future new models.

6. Capability Accumulation and Industry Insights Behind Day-0 Adaptation

Achieving Day-0 adaptation for an ultra-large parameter model like Kimi K3.1 Pro is not an overnight “emergency response,” but rather the result of long-term investment by the platform in operator library coverage, memory management, and parallel strategies. For AI infrastructure teams, this case provides the following reusable experiences:

1. Establish a Universal Model Feature Library: Once a platform has adapted several mainstream models, it should accumulate optimization configuration templates corresponding to common model architecture patterns (e.g., DeepSeekMoE, Llama dense attention). When a new model is open-sourced, applying a highly similar template and fine-tuning it can compress adaptation time from weeks down to 1-2 days.

2. Early Intervention of Quantization and Distillation: Many LLMs release quantized or distilled versions alongside their open-source launch. Adaptation teams should proactively request these versions rather than insisting on using dense FP16 models. Lightweight versions often perform more favorably on domestic hardware and can quickly produce performance baseline data.

3. Prioritize Ecosystem Compatibility Over Reinventing the Wheel: Communication interfaces, operator standards, and runtime APIs should maintain binary or source-code level compatibility with mainstream ecosystems as much as possible. This is not only key to reducing the integration costs for third-party LLMs but also the core driving force for attracting developers to migrate from other platforms.

Conclusion

From the release of a 3-trillion parameter model to the Day-0 adaptation by a domestic GPU, the gap bridged is not merely a matter of a dozen hours of engineering time, but rather the culmination of long-term hardware-software co-design. As model parameters reach into the trillions and context windows scale to the millions, the competitive focus of AI computing platforms has shifted from “can it run” to “running stably, running fast, and deploying economically.” To secure a place in the existing AI infrastructure market, domestic GPUs must provide a software solution that truly “understands the model,” and this practice offers a valuable reference paradigm.