The Evolution and Cloud Distribution Paths of Open-Source Large Models: From Parameter Scaling to Revenue-Sharing Distribution

1. A Compressed Evolution Timeline

Observing the development pace of open-source large models over the past two years reveals a distinct characteristic: model iteration cycles have been drastically compressed, with parameter scaling, reasoning capabilities, and commercial financing advancing almost simultaneously. A typical evolutionary path generally unfolds as follows:

• Phase 1: Release and open-source a medium-scale foundation model to validate the architecture and training pipeline.
• Phase 2: Launch enhanced versions tailored for complex reasoning tasks, strengthening chain-of-thought and long-context capabilities.
• Phase 3: Complete multiple funding rounds and rapidly increase valuation to secure compute budgets for the next, larger-scale training run.
• Phase 4: Release open-source models with over a trillion parameters, becoming one of the largest models in the open-source camp at the time.
• Phase 5: Suspend new subscriptions for individual users due to inference resource constraints, prioritizing compute for enterprise-level and platform-side API calls.
• Phase 6: Onboard top-tier global public cloud platforms via a revenue-sharing model, establishing a new sample for the commercial distribution of open-source models.
• Phase 7: Advance private equity financing and prepare for public market listing, clarifying the capitalization path.

This timeline itself does not constitute a technical solution, but it highlights a critical issue: once the parameter scale of open-source models crosses the trillion threshold, the core engineering contradiction shifts from “can we train it?” to “can we serve it stably and economically?” This article explores this contradiction, discussing the engineering constraints and coping strategies for ultra-large-scale open-source models across three stages: training, inference, and distribution.

2. Engineering Constraints Brought by Parameter Scaling

2.1 Training Side: Memory and Communication Walls

When parameter scales reach the trillion level (e.g., 2.8 trillion), the first wall the training system hits is GPU memory. Taking standard mixed-precision training as an example, the memory overhead per parameter during training includes weights, gradients, optimizer states (such as the first and second moments of Adam), and activations. Even with ZeRO-style sharding strategies, the memory of a single GPU still struggles to hold the complete state partitions. It requires a combination of tensor parallelism, pipeline parallelism, and data parallelism.

A typical 3D parallel configuration can express its sharding logic with the following pseudocode:

# Pseudocode: 3D parallel sharding illustration (not a specific framework API)
mesh = DeviceMesh(shape=(tp_size, pp_size, dp_size))
def shard_model(model, mesh):
    # Tensor Parallelism: shard weight matrices within a single layer
    model = tensor_parallel_shard(model, mesh.tp)
    # Pipeline Parallelism: split layers across different stages
    model = pipeline_stage_split(model, mesh.pp)
    # Data Parallelism: replicate and synchronize gradients
    model = data_parallel_replicate(model, mesh.dp)
    return model
# Key constraints: tp_size is limited by the single-node NVLink domain size
# pp_size is constrained by pipeline bubble rate and memory recomputation strategies
# dp_size is constrained by global batch size and convergence

The second wall is communication. Tensor parallelism relies on high-bandwidth interconnects; cross-node tensor parallelism significantly amplifies communication overhead. Pipeline parallelism introduces pipeline bubbles—the more stages in the pipeline, the higher the bubble ratio, requiring gradient accumulation and interleaved scheduling to dilute the cost. The common engineering practice is to restrict tensor parallelism within a single-node high-bandwidth domain, using pipeline and data parallelism for cross-node scaling, while trading activation recomputation for memory and using selective recomputation to reduce the recomputation cost.

2.2 Data Side: Quality Filtering Over Scale Stacking

Training a trillion-parameter model typically requires tens of trillions of tokens. At this scale, the focus of data engineering is not “accumulating volume” but deduplication, denoising, quality scoring, and domain ratio balancing. Common practices include near-duplicate removal based on MinHash or SimHash, classifier-based quality filtering, rule-based content cleaning, and setting sampling weights by domain. Data ratio imbalances directly reflect in downstream evaluations, so ratio ablation studies must be conducted on smaller proxy models before scaling up to full training.

2.3 Parallelism Strategy Trade-offs Table

Parallelism Dimension	Primary Problem Solved	Typical Constraints	Common Trade-offs
Tensor Parallelism	Single-layer weights too large	High-bandwidth interconnect domain size	Restrict within a single node, prioritize bandwidth
Pipeline Parallelism	Many layers, cannot fit on one GPU	Bubble rate, scheduling complexity	Combine with interleaved scheduling and gradient accumulation
Data Parallelism	Throughput scaling	Global batch size and convergence	Combine with sharded optimizers at large scales
Sequence Parallelism	Long-context activation memory	Attention computation communication	Use in combination with tensor parallelism

3. Inference Side: Why Subscriptions Get Paused

When model parameters reach the trillion level, the cost structure of inference undergoes a qualitative change. A simplified estimation illustrates this:

Single request cost ≈ (Input token count × Prefill compute per token
              + Output token count × Decode compute per token) × Unit compute cost
Where the decode phase is a memory bandwidth bottleneck:
Generating each token requires reading the active weights (without quantization and batching)
→ Larger batch sizes dilute the weight reading cost per token
→ But larger batches increase Time To First Token (TTFT) and memory footprint

This leads to two direct consequences:

1. Unit costs are controllable under high concurrency, but extremely high under low concurrency. Individual user requests are sparse, sessions are short, and traffic has high peak-to-valley fluctuations, making it difficult to form stable large batches. The unit token cost is thus much higher than that of enterprise-level batch API calls.
2. With limited compute budgets, resource prioritization is mandatory. When training the next-generation model and servicing the current model compete for GPU resources simultaneously, pausing new B2C subscriptions to prioritize enterprise contracts and platform-side calls is a typical resource scheduling decision, not merely a product strategy.

Engineering methods to alleviate inference costs include weight quantization (e.g., 8-bit/4-bit and lower-bit schemes), KV Cache reuse and paged management, prefix caching, continuous batching, speculative decoding, and request-priority scheduling. While these combinations can significantly reduce unit token costs, they cannot change the fundamental structure of “high costs under sparse traffic.”

4. Distribution Path: Technical Implications of Revenue-Sharing Models

The fact that ultra-large-scale models onboard top-tier public cloud platforms via a revenue-sharing model is a signal worth interpreting from an engineering perspective. It indicates at least three things:

4.1 Decoupling Model Side from Cloud Side

Model providers no longer need to build out their entire inference clusters. Instead, they deploy model weights and inference services into the cloud platform’s managed inference environment. The cloud side handles elastic scaling, GPU scheduling, monitoring, alerting, and billing. This decoupling allows model providers to convert Capital Expenditure (CapEx) into Operational Expenditure (OpEx), shifting the pressure of peak traffic expansion to the cloud platform’s resource pool.

4.2 Coupling of Billing Models and Call Chains

Revenue sharing means billing is no longer simply a “flat rate per token” but is tied to the actual revenue on the platform side. This introduces new technical requirements:

• Metering Precision: Token metering and attribution tags must be accurate down to the request level to avoid reconciliation discrepancies during multi-party settlements.
• Multi-Tenant Isolation: When different callers share the same inference cluster, resource quotas, rate limiting, and priority queues are required.
• Observability: Metrics like latency, error rate, and throughput must be exposed to both the model provider and the platform simultaneously for issue localization and cost attribution.

A simplified call chain can be abstracted as follows:

Client → Cloud Platform API Gateway (Auth, Rate Limiting, Metering Telemetry)
       → Inference Scheduling Layer (Queuing, Batching, Prioritization)
       → Model Inference Instance (Weight Loading, KV Cache Management)
       → Result Return + Usage Logging (Token Count, Latency, Attribution Tags)

4.3 Practical Impact on Developers

For consumers, the direct benefits of distributing models via public cloud are: standardized integration, elastic capacity, and unified billing. Developers do not need to build their own inference clusters; they can access trillion-scale model capabilities via universal API calls while leveraging the cloud’s monitoring and logging for cost analysis. Note that cold starts and scaling for ultra-large models can incur minute-level delays, so latency-sensitive scenarios should incorporate pre-heating, resident instances, and fallback strategies.

5. Engineering Practice Recommendations

Based on the above analysis, here are actionable best practices:

1. Choose model scale by scenario, not just sheer size. Trillion-scale models are suited for complex reasoning and long-document comprehension. High-frequency, short-request scenarios should prioritize small-to-medium models, routing traffic via a gateway layer.
2. Stress-test inference costs before setting capacity. Load test with real traffic distributions (not uniform requests), focusing on P99 latency and batching efficiency to avoid blind scaling based on peak values.
3. Leverage caching and batching. Prefix caching yields significant benefits for scenarios with fixed system prompts; continuous batching dramatically improves throughput but requires evaluating its impact on TTFT.
4. Establish a cost attribution system. Embed tenant tags and usage logs in the call chain to facilitate cost accounting by business line and prepare for revenue-sharing settlement models.
5. Design fallbacks and circuit breakers. Ultra-large model services may experience queuing during traffic spikes. Applications should implement timeouts, retries, and fallback strategies to smaller models.
6. Verify open-source licenses and deployment formats. Commercial terms, weight distribution methods, and precision loss in quantized versions must be verified during the selection phase.

6. Conclusion

From parameter scaling to revenue-sharing cloud distribution, the evolution of open-source large models has shifted from isolated technical breakthroughs to systemic engineering challenges. The training side must resolve the joint constraints of memory and communication; the inference side must address the cost structure under sparse traffic; the distribution side must solve the coupling of metering, isolation, and settlement. For developers and enterprises, understanding these three layers of constraints holds more long-term value than memorizing a specific model’s parameter count. Models will continue to iterate, but the three engineering principles of controllable costs, observable chains, and elastic capacity will remain the foundation for deploying ultra-large-scale models.