Accelerating Trillion-Parameter Model Inference: Hardware-Software Co-Optimization Practices

1. Background: New Infrastructure Demands of Ultra-Large Models

As large language models (LLMs) scale from hundreds of billions of parameters into the trillion range and beyond—toward three trillion—the engineering complexity of both training and inference increases significantly. Compared with conventional 10B- and 100B-parameter models, trillion-scale models typically adopt sparse activation architectures: although the total parameter count is enormous, only a subset of parameters is activated in each forward pass. This design controls compute cost but introduces new deployment challenges:

• Model weights cannot fully reside in a single card’s memory, requiring well-designed multi-card sharding and weight loading strategies;
• Sparse expert routing requires the compute framework to efficiently schedule dynamic computation graphs;
• Million-token context windows place higher demands on attention mechanisms, KV cache management, and long-sequence parallelism;
• Agent-style tasks often involve multi-turn tool calls and long reasoning chains, making them more sensitive to time-to-first-token and throughput stability.

Bringing ultra-large models into production is therefore no longer just about “getting inference to run.” It requires a systematic solution spanning operator adaptation, memory management, communication strategies, and scheduling mechanisms. For in-house accelerator hardware, targeted optimization based on the instruction set, software stack, and interconnect topology is also required to fully exploit the hardware’s capabilities.

2. Model Architecture Characteristics and Inference Challenges

2.1 Compute Characteristics of Sparse Activation

Trillion-scale models are typically composed of a large number of expert subnetworks. Each expert is relatively compact; experts share the input embedding and attention layers, but during the feed-forward stage, tokens are routed to only a few experts. This “large capacity, small activation” characteristic means inference compute does not scale linearly with total parameters, but the architecture is highly sensitive to weight loading speed, communication bandwidth, and scheduling latency.

From a computational perspective, sparse model inference can be broken into three stages:

1. Routing stage: compute expert selection probabilities for each input token and determine the set of active experts for this step;
2. Expert computation stage: dispatch tokens to the corresponding expert subnetworks and execute matrix multiplications, activation functions, and other operations;
3. Merge stage: aggregate expert outputs with weighting, add them to the residual path, and pass the result to the next layer.

The second stage can run in parallel across accelerator cards, but the first and third stages involve cross-card communication and synchronization. Reducing communication overhead and improving load balancing is therefore one of the core problems in inference optimization.

2.2 KV Cache Pressure from Long Contexts

Supporting million-token contexts means the model must maintain a very large KV cache during inference. When sequence lengths reach hundreds of thousands or even a million tokens, the KV cache can occupy hundreds of gigabytes or even terabytes of memory. In multi-turn conversations and long-horizon Agent tasks, the cache keeps growing with each interaction round.

To address this, the industry commonly uses paged KV caches, quantization-based compression, and combinations of sliding-window and global attention to reduce memory footprint. Multi-card inference also requires a sensible KV sharding strategy, so that each card stores only the cache segments relevant to its own computation, with on-demand exchange over high-speed interconnects.

2.3 Dual Constraints of Agent Workloads: Latency and Stability

Long-horizon Agent tasks typically follow a loop of “model inference — tool call — result parsing — inference again.” A single request may require dozens of model calls, each with varying output lengths, and some calls are extremely latency-sensitive. If the inference service exhibits noticeable performance jitter or high time-to-first-token during long tasks, the entire Agent chain slows down.

Deployment solutions for Agent scenarios therefore need to deliver two capabilities: first, compressing single-inference latency through operator fusion, graph optimization, and scheduling improvements; second, improving overall throughput through sensible batching and queue management while staying within latency budgets.

3. Key Steps in Software Stack Adaptation and Inference Path Migration

3.1 Unified Programming Model and Operator Compatibility Layer

When adapting a new model to custom accelerator hardware, the maturity of the software stack directly determines the adaptation cycle and final performance. A well-designed stack typically includes a unified programming model, a foundational operator library, a graph compiler, and runtime components. Developers can write custom operators through general programming interfaces, and the compiler maps high-level computation graphs onto low-level hardware instructions.

For sparse activation models, the software stack needs native support for:

• Sparse tensor representation and transfer;
• Graph representation of conditional computation and dynamic control flow;
• Cross-device communication primitives and synchronization mechanisms;
• Mixed-precision computation and low-bit quantization.

In practice, an operator compatibility layer can map standard operators in the model to optimized implementations on the accelerator, while custom operators for model-specific structures—such as dynamic routing and expert dispatch—can be developed through the unified programming model. This reuses existing optimizations while covering the new model’s special requirements.

3.2 Graph Compilation and Operator Fusion

A critical part of inference optimization is computation graph optimization. After obtaining the model’s graph structure, the compiler can apply a series of hardware-independent and hardware-dependent optimization passes:

1. Constant folding and dead code elimination: remove computation paths not needed for inference;
2. Operator fusion: merge adjacent matrix multiplications, bias additions, and activation functions into single operators, reducing intermediate tensor memory reads and writes;
3. Layout optimization: select the optimal tensor memory layout for the hardware, such as channel-first or blocked layouts;
4. Memory planning: allocate reusable buffers for intermediate tensors at compile time, reducing runtime allocation overhead.

For sparse models, operator fusion must also account for the dynamic nature of routing computation. For example, when multiple expert subnetworks share the same structure, expert computation for tokens in the same batch can be fused into batched matrix multiplication, increasing compute intensity and hardware utilization.

3.3 Multi-Card Sharding and Parallelism Strategies

A three-trillion-parameter model cannot be loaded entirely on a single accelerator card and must use multi-card parallelism. Common strategies include:

• Tensor parallelism: split weight matrices within a single Transformer layer along rows or columns across multiple cards, with each card handling part of the computation;
• Pipeline parallelism: split the model into stages by layer, assign different stages to different cards, and use micro-batch pipelining to reduce bubbles;
• Expert parallelism: distribute different expert subnetworks across different cards, activating the corresponding experts on demand after routing;
• Data parallelism: each card holds an identical model replica and processes different input batches.

In real deployments, these strategies are usually combined. For sparse models, expert parallelism naturally fits the architecture—different experts can be placed on different compute nodes, with token routing and result aggregation handled over high-speed interconnects. However, when expert loads are unbalanced, some cards may sit idle while others are overloaded, requiring routing adjustments or dynamic load balancing mechanisms.

4. Memory Management and Communication Optimization Practices

4.1 Weight Loading and Caching Strategies

Weights for a three-trillion-parameter model are enormous. Even with quantization compression, loading the full weights from host memory or local storage takes considerable time. To reduce service startup and model-switching latency, tiered loading and prefetching strategies can be applied:

• Organize model weights by layer or expert subnetwork, prioritizing the loading of entry layers and routing modules;
• During inference, prefetch weights for subsequent layers based on the current batch’s routing results;
• Establish a multi-level cache across accelerator memory, host memory, and local high-speed storage: hot weights stay in accelerator memory, warm weights reside in host memory, and cold weights load from storage on demand.

This approach keeps time-to-first-token within an acceptable range while avoiding frequent swapping caused by insufficient memory.

4.2 Tiered KV Cache Management and Reuse

In long-context scenarios, KV cache management is the focus of memory optimization. One feasible approach is dividing the KV cache into hot, warm, and cold tiers:

• Hot tier: caches for recently processed tokens, kept in accelerator memory for efficient attention computation;
• Warm tier: caches for earlier tokens, compressed and stored in accelerator memory or high-speed host memory;
• Cold tier: caches for the earliest tokens, stored with high compression ratios and decompressed only when needed.

In multi-turn conversations and Agent scenarios, caches for system prompts and previous conversation turns can be reused. By maintaining cache version identifiers, when an input prefix matches, existing KV caches can be reused directly, avoiding recomputation.

4.3 Reducing Communication and Synchronization Overhead

In multi-card inference, communication often becomes the performance bottleneck. Taking expert parallelism as an example, every inference step requires sending tokens to the cards hosting their routed experts, then aggregating results after computation. With many experts and large token batches, communication volume grows substantially.

Common techniques for reducing communication overhead include:

• Communication-computation overlap: execute communication operations asynchronously alongside local computation, using pipelining to hide communication latency;
• Batched communication merging: combine multiple small communications into one large communication to reduce launch overhead;
• Topology-aware scheduling: plan expert placement according to the accelerator interconnect topology, prioritizing high-bandwidth links for frequently interacting experts;
• Compressed-precision transfer: quantize tensors to low bit-widths before cross-card transfer, dequantizing at the receiver before computation.

These optimizations require the software stack’s communication library to provide flexible asynchronous interfaces and topology query capabilities, so that upper-layer scheduling policies can be aware of the underlying hardware connections.

5. Deployment Design for Long-Context and Agent Workloads

5.1 Tiered Queues and Priority Scheduling

When a single inference system serves both online conversations and offline batch tasks, it needs tiered scheduling capability. Long-horizon Agent tasks, with their multi-step call patterns, are more latency-sensitive than ordinary generation tasks. A multi-level queue design can help:

• High-priority queue: serves online requests with strict time-to-first-token requirements;
• Standard-priority queue: serves general online inference requests;
• Low-priority queue: serves offline batch tasks that can tolerate longer queuing times.

The scheduler dynamically adjusts throughput allocation across queues based on current hardware utilization, maximizing batch sizes within latency budgets.

5.2 Continuous Batching and Preemptive Execution

Static batching wastes resources; continuous batching allows new requests to join while existing sequences are still generating. For Agent scenarios, there are natural gaps between model calls—the model does not need to compute during tool execution. The scheduler can use these gaps to insert waiting requests, improving overall utilization.

Going further, preemptive execution can be introduced: when a high-priority request arrives, the currently executing low-priority step can be suspended at a safe point and its resources partially released, resuming after the high-priority request completes. This mechanism places high demands on the runtime’s task state saving and restoration capabilities, but it can significantly improve service quality under mixed workloads.

5.3 Elastic Memory Scaling for Long-Sequence Generation

Sequence lengths in long-context tasks can range from a few thousand to a million tokens. Reserving maximum-length KV cache space for every request wastes enormous amounts of memory. A more sensible approach is to allocate cache dynamically based on actual sequence length, triggering reclamation or compression when memory pressure rises.

For example, multi-level cache watermarks can be set: when accelerator memory usage reaches one threshold, low-bit compression is applied to the KV caches of earlier tokens; at a higher threshold, some cold-tier caches migrate to host memory; as pressure subsides, capacity is gradually restored. This elastic scaling mechanism adapts to the dynamic demands of long-sequence inference while preserving correctness.

6. Evaluation Dimensions for Adaptation Validation

After completing Day-0 adaptation, systematic validation is needed to ensure inference correctness and performance reach deployable levels. Evaluation typically covers the following dimensions:

6.1 Correctness Validation

Correctness is the primary prerequisite for inference adaptation. Build test sets covering typical input patterns and compare output differences between the reference implementation and the accelerator implementation. For generative models, use statistical distribution comparisons across multiple sampling runs rather than comparing single sampled token sequences. For long-context inputs, pay particular attention to the correctness of positional encodings, attention masks, and boundary conditions.

6.2 Performance Metrics

Key performance metrics include:

• Time to first token (TTFT): the time from request submission to the first generated token; this most directly affects the online interactive experience;
• Per-token latency: the average time to generate a single token, affecting total generation time for long text;
• Throughput: the total number of tokens or requests processed per unit time, reflecting overall hardware utilization efficiency;
• End-to-end task completion time: for Agent tasks, the completion time of the entire multi-step call chain is more meaningful than single-step latency.

These metrics should be measured across different batch sizes, sequence lengths, and concurrency levels to form a performance matrix.

6.3 Stability and Regression Testing

Performance stability under long-duration continuous operation is equally important. Stress tests that simulate real traffic patterns can reveal memory leaks, gradual performance degradation, or intermittent timeouts. Additionally, regression testing is required after software stack upgrades to ensure the inference capabilities of existing models remain unaffected.

7. Summary and Outlook

Deploying three-trillion-parameter models for inference is a systems engineering effort spanning model architecture understanding, software stack adaptation, operator optimization, memory management, communication scheduling, and performance validation. Starting from the architectural characteristics of sparse activation models, this article outlined the key paths and tuning methods for Day-0 adaptation, with a focus on deployment strategies for long-context and Agent workloads.

As ultra-large models continue to evolve toward higher parameter counts, longer contexts, and stronger reasoning capabilities, accelerator hardware must keep improving in software stack maturity, interconnect bandwidth, memory capacity, and programming usability. Inference frameworks, meanwhile, need to strengthen support for dynamic computation graphs, adaptive scheduling, and elastic resource management. Only through tighter coordination between hardware capability, foundational software, and upper-layer frameworks can the barrier to engineering ultra-large models be lowered, enabling more developers and enterprises to deploy high-performance inference services at manageable cost.