Running a 3-Trillion-Parameter LLM on a Domestic GPU: A Day-0 Adaptation Practice

Background: Inference Challenges Posed by a 3-Trillion-Parameter Model

As large language models scale from hundreds of billions to trillions or even 3 trillion parameters, the pressure on inference infrastructure grows exponentially. The open-source community recently welcomed K3.1 Pro, a flagship model with 3 trillion parameters and support for a 1-million-token ultra-long context. After large-scale post-training and reinforcement learning, the model demonstrates capabilities comparable to top-tier closed-source models in code generation, 3D creation, financial research, knowledge Q&A, and long-horizon agent tasks. For example, it scored 65.7% on Terminal-Bench 4.0 and 61.2% on long-horizon code agent tasks, a significant improvement over the previous generation.

However, scaling parameters brings not only capability gains but also severe demands on the underlying compute platform. Three trillion parameters means model weight storage alone reaches tens of TB (at mainstream precision), far exceeding the memory capacity of a single GPU. During inference, KV cache memory usage also scales linearly with sequence length—a 1-million-token context can require hundreds of GB of KV cache. These hard constraints mean that model adaptation is no longer a simple engineering task of “load weights, run forward pass,” but a systematic effort involving distributed parallelism strategies, memory hierarchy planning, operator optimization, and communication scheduling.

This article is based on a real-world practice of completing Day-0 adaptation of K3.1 Pro on a domestic GPU platform equipped with an AI training-and-inference integrated accelerator card and a self-developed software stack toolchain. It breaks down the key stages of large-model inference adaptation to provide a transferable methodology for developers facing similar challenges.

1. Deconstructing Model Architecture and Computational Characteristics

The first step in adaptation is not coding, but a careful analysis of the model’s architectural features and computational patterns. As a 3-trillion-parameter MoE (Mixture of Experts) model, K3.1 Pro differs significantly from dense models.

1.1 Sparse Activation in MoE Architecture

The core idea of MoE is to partition the model into multiple “expert” sub-networks, activating only a small subset during each inference. In a typical configuration—say, 200 experts with Top-2 routing—the compute per forward pass is roughly 1/100 that of a dense model. This sparse activation greatly reduces computational cost, but when all expert parameters must still be loaded into memory or VRAM and experts are dynamically scheduled based on routing results, it places unique demands on memory capacity, bandwidth, and scheduling flexibility.

1.2 Memory Amplification from Long-Sequence Modeling

Support for a 1-million-token context means the attention mechanism’s computational complexity is quadratic in sequence length. Even with IO-aware optimizations like FlashAttention, KV cache storage still grows linearly with sequence length. For a 3-trillion-parameter model, memory planning for KV cache plus model weights must be precise down to the GB level—any coarse allocation can lead to OOM or severe fragmentation.

1.3 Key Operator Characteristics in the Inference Path

From a computational graph perspective, compute-intensive operators (e.g., matrix multiplication, attention) and memory-intensive operators (e.g., expert routing, normalization, activation functions) interleave in the inference path. In agent task scenarios especially, long-horizon reasoning involves frequent context switching and state management, stressing the inference engine’s dynamic scheduling capability.

2. Adaptation Strategy: From Model Migration to Stable Operation

After architecture analysis, adaptation enters the substantive phase. The overall workflow can be broken down into the following key paths.

2.1 Model Weight and Computational Graph Migration

When hosting a third-party model on a domestic GPU platform, the first priority is to establish the mapping channel between the computational graph and the underlying instruction set. The core approach here is model migration based on a self-developed software compilation toolchain.

Specifically, the adaptation team needs to:

• Operator mapping and replacement: Find equivalent implementations in the software stack for general operators used in the model, or recombine them into platform-friendly operator forms through fusion/splitting. Focus on high-frequency operators such as attention kernels, MoE routing, and FFN.
• Graph optimization and compilation: Perform operator fusion (e.g., merging consecutive linear transforms and activations into a single kernel), dead code elimination, and constant folding. The compilation process must be tuned for GPU register allocation and shared memory usage.
• Precision alignment verification: Use a combination of layer-by-layer output comparison and whole-model error statistics to ensure the relative error between the migrated model output and the reference implementation is within a reasonable range (typically a threshold of 1e-3).

2.2 Memory Planning and Hierarchical Storage Strategy

Facing the memory pressure of 3 trillion parameters, a single-tier memory management approach is far from sufficient. In practice, a three-tier storage architecture was adopted:

Storage Tier	Capacity	Access Latency	Primary Use
HBM VRAM	Tens of GB to over 100 GB	Low (<1μs)	Active expert weights, hot KV cache, intermediate activations
Host memory	Hundreds of GB to TB	Medium (μs–ms)	Full model weight storage, cold KV cache offload
Persistent storage	Several TB	High (ms)	Model backup, checkpoint storage

During inference, an asynchronous prefetch mechanism moves expert weights needed for the current step from host memory to VRAM, while writing back inactive VRAM data to host memory. The scheduler predicts expert activation for the next few steps based on routing distribution and issues prefetch instructions in advance, maximizing the masking of memory copy latency.

For VRAM fragmentation management, physically contiguous allocation based on large pages and a custom memory pool reuse mechanism are used. For KV cache—which grows dynamically and has variable sizes—a block-based storage structure was specifically designed to avoid fragmentation caused by frequent VRAM allocation and deallocation.

2.3 Distributed Parallelism: Combining Tensor Parallelism and Expert Parallelism

Three trillion parameters cannot reside on a single GPU, so distributed parallelism is inevitable. In the K3.1 Pro adaptation, a combination of two parallelism strategies was emphasized.

Tensor parallelism: Matrix weights in Transformer layers are split by row or column across multiple GPUs. Each GPU holds only a weight slice and completes partial computation, with results aggregated via AllReduce. Tensor parallelism significantly reduces per-card memory peaks, but its communication overhead grows with the parallelism dimension, so in practice the tensor parallelism dimension is typically kept within 8.

Expert parallelism: MoE expert distribution is naturally suited for load-balanced parallel partitioning. Under expert parallelism, each GPU holds a different subset of expert parameters. The routing layer routes tokens to the GPUs holding target experts via All-to-All communication based on token-to-expert assignment. Compared to dense model communication patterns, MoE’s All-to-All communication is more sensitive to network bandwidth and latency, so topology design should minimize cross-node communication.

In practice, the scheduler employs two-stage pipeline overlap: while computing the current layer’s matrix multiplication, it prefetches input tokens needed for the next layer’s routing. Through compute-communication overlap, communication overhead is effectively hidden under compute time, with measured throughput gains of 30%–45%.

2.4 Dynamic Batching and Long-Horizon Task Management

For long-horizon agent tasks supporting 1M context, the core challenge for inference serving lies in dynamically varying sequence lengths and computation allocation across different requests. Fixed batching strategies easily lead to VRAM waste or idle compute.

Continuous batching is especially critical in this scenario. The core idea is to manage rejection and scheduling at the token granularity, allowing new requests to be inserted into gaps of ongoing batch inference. Key implementation points include:

• Iteration-level scheduling: Split the batch inference process into multiple iterations, checking after each iteration for new or completed requests, and dynamically adjusting batch size.
• Prefill-decode separation: KV cache is allocated on demand during the prefill phase, while the decode phase uses shared prefix caching (similar to RadixTree or PagedAttention mechanisms) to support fast deduplication and cache reuse.

3. Software Stack Support: Ecosystem Compatibility and Performance Tuning

Unlocking hardware capability requires deep software stack support. This adaptation is based on the platform’s self-developed software stack, whose core layer consists of three parts: compiler, runtime, and tool libraries.

3.1 Compiler Layer

The compiler maps high-level computational graph languages to low-level hardware instructions. A key point in the adaptation process is enabling the compiler to recognize and efficiently compile common patterns in the model such as prefill and sparse attention, providing high-performance matrix multiplication and vectorized operator implementations through an intrinsic function library.

3.2 Runtime and Communication Library

The runtime environment provides foundational capabilities for model inference such as process management, VRAM management, and error handling. Notably, for MoE models, the runtime must also provide dedicated memory registration and communication buffer pooling mechanisms to reduce the number of data copies between GPU and host during All-to-All communication.

3.3 Debugging and Analysis Tools

Another important aspect of adaptation is performance profiling. Through a graphical profiler that visualizes kernel execution timelines, GPU utilization, and VRAM allocation curves, the development team can quickly locate bottlenecks. For example, the profiler revealed that imbalanced routing for a certain expert caused a single GPU’s utilization to drop below 60%; the team then adjusted the routing hash strategy, improving overall system throughput by about 17%.

4. Verification and Performance Evaluation

Day-0 adaptation is not just about loading a model to make it runnable; it also includes a rigorous verification process. The verification strategy in this adaptation covered three dimensions:

Correctness: Using a reference-output comparison method, multiple typical inputs (covering code generation, mathematical reasoning, long-text summarization, etc.) were selected to compute the cumulative log-likelihood difference of output tokens. A difference of less than 5% was set as the passing threshold.

Performance: In an MTT S5000 cluster environment, the core metrics were time-to-first-token (TTFT), time-per-output-token (TPOT), and throughput (tokens/s). In a single-card configuration with multi-process parallelism distributing MoE experts evenly across multiple cards, measured throughput in long-sequence scenarios reached hundreds of tokens/s, meeting basic production requirements.

Stability: Multi-day uninterrupted stress testing was conducted, monitoring risks such as VRAM leaks, communication timeouts, and operator exceptions. During testing, an overflow issue in a normalization operator during FP16 accumulation was identified and resolved by switching to a mixed-precision path (FP16 matrix multiplication, FP32 accumulation).

5. Practical Insights and Reusable Experience

Reviewing the entire K3.1 Pro adaptation process, the following insights offer direct reference value for future large-model adaptation work:

5.1 Architecture Understanding Comes First

Before adaptation, a thorough analysis of model architecture, weight layout, and activation numerical ranges is essential. For example, MoE’s top-k routing logic directly affects communication pattern design, and rotary position encoding in long-context scenarios imposes additional numerical stability requirements on operators. Architecture-level decision mistakes are often extremely difficult to correct later.

5.2 Memory Planning at “Minute-Level” Granularity

For 3-trillion-scale models, VRAM resources are extremely tight. It is recommended to pre-compute an operator-level VRAM usage table during the planning phase, then combine it with dynamic allocation strategies for hierarchical management. Any single misallocation can trigger cascading OOM.

5.3 Communication Optimization Is a Performance Multiplier

In distributed inference, communication time often accounts for 30%–40% or more of total time. Prioritizing optimizations such as compute-communication overlap, message granularity adjustment, and communication primitive selection (AllReduce vs. ReduceScatter) often yields higher returns than optimizing compute operators themselves.

5.4 Toolchain Maturity Determines Adaptation Efficiency

A software stack with automatic operator tuning, VRAM visualization and tracing, and distributed debugging capabilities can greatly reduce the cost of manual troubleshooting. It is recommended that platform teams continue investing in toolchain development to support emerging new architecture models.

Conclusion

Completing Day-0 adaptation of a 3-trillion-parameter model on a domestic GPU platform demonstrates not only improved hardware compute but also a leap in software stack maturity. From architecture analysis to parallelism strategy design, from fine-grained memory management to communication latency hiding, every step requires deep understanding of system-level performance and global trade-offs. As model parameter scales continue to evolve toward the 10-trillion level, adaptation work will face even more severe memory wall and communication wall challenges—and the methodologies and engineering experience accumulated today are the solid stepping stones toward the next generation of large-model infrastructure.