Optimizing Inference for 3-Trillion-Parameter LLMs on Domestic GPUs: A Hands-On Guide

1. Before You Start: Three Core Challenges and Response Strategies

When large language models scale to around 3 trillion parameters and undergo extensive post-training and reinforcement learning, their capabilities extend to code generation, 3D creation, financial analysis, and agent workloads with million-token contexts. Achieving Day-0 adaptation on domestic AI accelerators requires developers to first understand three major obstacles and define a clear response for each.

• Memory barrier: Even with INT8 or FP8 quantization, model weights alone must be distributed across multiple accelerators. Combined with activations and million-token KV cache, a single card’s memory is far from sufficient. Strategy: use tensor parallelism (TP) and pipeline parallelism (PP) to distribute memory and compute across cards.
• Long-horizon agent scheduling latency: Multi-step reasoning continuously accumulates context, sharply increasing time-to-first-token. Strategy: optimize the attention computation path and introduce techniques such as speculative decoding to reduce memory-access bottlenecks.
• Quantization accuracy risk: Low-bit quantization compresses memory usage but can degrade accuracy. Strategy: establish an FP16 baseline first, then reduce quantization step by step and validate accuracy on small-scale tasks.

In addition, this scenario involves model licensing and data security. Developers must confirm whether the model weights are governed by an open source license or commercial authorization. User data used during inference must also comply with applicable data security regulations.

2. Migrating the Compute Graph and Fusing Core Operators: From CUDA to Domestic Instruction Sets

The first step in adapting to new hardware is a systematic migration from the low-level instruction set to the upper runtime layer, rather than relying on hard binding to the existing ecosystem.

2.1 Compute Graph Rewriting and Environment Checklist

Developers need to remap critical paths in mainstream inference frameworks based on CUDA-like or proprietary compute architectures provided by domestic vendors, such as MUSA. The core step is to replace compute graph nodes for modules such as attention, LayerNorm, and FFN with hardware-native operators.

Pre-deployment checklist:

Component	Recommended Version / Requirement	Notes
Domestic compute SDK, such as MUSA	>=1.x.x, depending on the vendor release	Confirm compatibility with the target inference framework
GPU driver	>= the minimum driver version required by the SDK	Driver and SDK versions must match strictly
Domestic communication library	Use the vendor-provided NCCL-equivalent library, such as DC-NCCL	Confirm PCIe P2P or proprietary interconnect support
Inference framework adapter	Official domestic-hardware branch of the inference framework	Monitor community maintenance status and version synchronization

2.2 FlashAttention Fusion Implementation

For million-token contexts, a low-level mapping of FlashAttention must be implemented. The core idea is to use tiling to merge QKV matrix multiplication, softmax, and masking into a single kernel, dramatically reducing HBM reads and writes. The following pseudo-code adds tiling parameters and domestic API call examples:

def fused_attention_forward(q, k, v, attention_mask):
    """
    FlashAttention fusion pseudo-code (illustrative interface, not a full implementation):
    1. tile_size is tuned according to hardware SRAM size, typically 64 or 128
    2. Load Q/K/V tiles into shared memory using native APIs such as musa_core_load
    3. Perform matrix multiplication and softmax inside shared memory
    4. Accumulate results and write back to HBM to avoid repeatedly moving intermediate results
    A production deployment must call native hardware instructions, such as the MUSA Core API.
    """
    # tile_size = 64  # Tune according to hardware SRAM size
    # for each tile:
    #   q_tile = musa_core_load(q, tile_start, tile_size)
    #   k_tile = musa_core_load(k, tile_start, tile_size)
    #   v_tile = musa_core_load(v, tile_start, tile_size)
    #   attn = softmax(q_tile @ k_tile.T + mask) @ v_tile
    #   accumulate(output, attn)
    pass

2.3 Paged KV Cache Management

Traditional contiguous memory allocation can cause severe memory fragmentation under million-token contexts. A paging mechanism similar to PagedAttention must be introduced: divide the KV cache into fixed-size pages, allocate them on demand, and support irregular context growth. In practice, a reasonable page size, such as 64 tokens per page, can help maintain high memory utilization. However, the exact result depends on the hardware model, context distribution, and batching strategy. Developers should tune and validate based on measurements in their own environment.

3. Engineering-Level Optimization: Parallel Communication, Dynamic Scheduling, and Mixed Precision

3.1 Configuring Tensor Parallelism and High-Speed Communication Libraries

Inference for a 3-trillion-parameter model typically requires four to eight accelerators working together. Tensor parallelism (TP) requires a clear partitioning strategy: split attention heads evenly across cards, for example 64 heads across 8 cards giving 8 heads per card, and apply column-wise partitioning to the FFN hidden layer to balance communication and compute load. All-reduce communication across cards must rely on hardware-level high-speed interconnect, such as PCIe P2P or a proprietary interconnect, and should use a domestic communication library equivalent to NCCL, such as the vendor’s official DC-NCCL implementation, to keep communication latency within an acceptable range.

3.2 Dynamic Batching and Prefill/Decode Disaggregated Scheduling

In code generation scenarios, request lengths vary widely. The system must support dynamic batching: new requests should not interrupt ongoing generation sequences, and idle compute should be used to run prefill computation. Production environments also require timeout eviction and priority queue logic. The core scheduling logic is as follows:

def schedule_batch(active_requests, waiting_queue):
    """
    Prefill/decode disaggregated scheduling illustration:
    1. Prioritize short requests in the waiting queue for prefill
    2. Reuse the same batch for autoregressive decoding
    3. Dynamically adjust batch_size to fully utilize GPU compute
    4. Evict timed-out requests and insert high-priority requests at the queue head
    """
    # Timeout eviction
    active_requests = evict_timeout(active_requests, timeout_ms=5000)
    # Priority queue sorting
    waiting_queue = sort_by_priority(waiting_queue)
    prefill_batch = select_shortest(waiting_queue, max_tokens=2048)
    decode_batch = active_requests
    # Dynamically merge based on remaining compute capacity
    return merge_if_fits(prefill_batch, decode_batch)

3.3 Quantization Options and Accuracy Risk Notes

Quantization Precision	Memory Savings vs FP16	Inference Speedup	Accuracy Loss Risk	Suitable Scenarios
FP16/BF16	Baseline	Baseline	Very low	High-accuracy requirements
INT8	~50%	1.5-2x	Low	General-purpose inference
FP8	~50%	1.8-2.5x	Low to medium	Post-training quantization
INT4	~75%	2-3x	Medium to high	Severely memory-constrained scenarios

Risk note: INT4 quantization may significantly degrade accuracy on code generation and complex reasoning tasks. It is recommended to validate on small-scale tasks before promoting to production. Domestic accelerators must provide native mixed-precision support in compute units, and dequantization operators must be fusible into the preceding matrix multiplication. Quantization measurements should also be clearly defined: memory savings use FP16 as the baseline, and speedup is measured under the same batch size and sequence length.

4. Business Validation: How to Run Benchmarking Correctly

Adaptation results must ultimately be validated through end-to-end business tests. When running benchmarks, record the following information clearly and accurately:

• Benchmark name: public benchmarks such as HumanEval or Terminal Bench
• Test hardware: specific accelerator model, card count, and interconnect topology
• Model version: quantization scheme and post-training stage
• Evaluation methodology: pass rate or score calculation, and whether few-shot inference is included

You can run your own tests using public benchmarks such as HumanEval and Terminal Bench. The following benchmark data collection template can be used as a reusable checklist:

Field	Description	Example
batch_size	Number of requests per inference step	32
seq_len	Total input plus output sequence length	4096
TTFT	Time to first token in milliseconds	120
TPS	Tokens generated per second	45
Peak memory per card (GB)	Maximum memory usage per accelerator	48
Quantization scheme	Deployed precision	INT8

In agent scenarios such as multi-turn tool calling, the compute graph is frequently interrupted and resumed. The software stack must guarantee state persistence and seamless continuation. IDE plugin scenarios depend on hardware support for advanced algorithms such as speculative decoding and require very low time-to-first-token.

5. FAQ

Q1: What is the most common mistake when adapting large models to domestic GPUs?
A: Directly reusing CUDA code without instruction-set-level rewriting, which causes operators to fail during mapping or deliver very poor performance. Always start by migrating the low-level compute graph and confirm that SDK and driver versions match.

Q2: How should I choose the right quantization precision?
A: Start with an FP16 baseline and record reference performance. If memory is constrained, gradually move down to INT8. For accuracy-sensitive tasks such as financial analysis, avoid INT4. Every quantization level must pass validation on small-scale tasks before production rollout.

Q3: Should TP partitioning target attention heads or FFN?
A: Usually both are partitioned at the same time. Attention heads are evenly split across cards, while the FFN hidden layer uses column-wise partitioning to balance communication and compute load.

6. Summary and Outlook

Day-0 adaptation for a 3-trillion-parameter model is a full-stack effort spanning chip instruction sets, low-level runtimes, distributed communication, and upper-layer scheduling. Through deep coordination between the domestic software stack and domestic accelerators, developers can achieve usable performance in agent scenarios such as code generation and financial analysis under specific conditions, although actual performance must be validated through measurement. Future optimization will focus on heterogeneous resource scheduling and support for linear-complexity models with longer contexts. Building an open and standardized low-level ecosystem remains a long-term shared challenge. Developers should also continue to monitor model licensing compliance and data security requirements to keep the entire pipeline compliant and controllable.