Technical Deep Dive
The core technical challenge behind token standardization is the 'heterogeneity tax'—the overhead of making diverse AI accelerators speak a common language. Today's domestic AI chip landscape includes GPGPUs (e.g., from Moore Threads, MetaX), NPUs (e.g., from Cambricon, Horizon Robotics), and ASICs (e.g., from Bitmain's Sophon, and various startups). Each architecture has a unique memory hierarchy, instruction set, and operator library. For example, Cambricon's MLU270 uses the BANG C language with its own tensor operators, while Moore Threads' MTT S80 relies on CUDA-compatible MUSA. This forces model developers to maintain multiple code paths or rely on intermediate frameworks like TVM or MLIR.
Token standardization solves this by introducing a virtual instruction set that sits above hardware-specific stacks. Think of it as a 'bytecode for AI inference.' The key components are:
- Token Definition: A standardized unit representing the compute cost of generating one output token for a reference model (e.g., a 7B-parameter LLM with 2048 context length). This is analogous to 'vCPU' in cloud computing but specialized for transformer inference.
- Token Metering: A runtime that measures actual compute consumption (FLOPs, memory bandwidth, latency) and normalizes it to token equivalents. This requires hardware counters or profiling hooks.
- Token Scheduling: An orchestration layer that maps token requests to available hardware based on real-time efficiency. This is similar to Kubernetes but for token-level resource allocation.
Several open-source projects are converging on this vision. The OpenToken repository (github.com/opentoken/opentoken, 2.3k stars) provides a reference implementation of a token metering library for PyTorch and ONNX Runtime. It profiles kernel execution time and memory access patterns to estimate token cost per model layer. Another project, TokenFlow (github.com/tokenflow/tokenflow, 1.1k stars), focuses on dynamic batching and scheduling across heterogeneous devices, using a priority queue to maximize throughput.
| Metric | Native CUDA (NVIDIA A100) | Native BANG C (Cambricon MLU370) | Token-Abstracted (OpenToken on MTT S80) |
|---|---|---|---|
| Throughput (tokens/sec) | 1,200 | 850 | 780 |
| Latency (ms/token) | 0.83 | 1.18 | 1.28 |
| Developer Effort (person-days) | 10 | 30 | 15 |
| Portability (models supported) | 100% | 60% | 95% |
Data Takeaway: Token abstraction introduces a ~8% throughput penalty versus native optimized code, but reduces developer effort by 50% and increases model portability to 95%. The trade-off is acceptable for most production scenarios where developer velocity matters more than peak performance.
Key Players & Case Studies
Several domestic players are driving token standardization, each with a distinct strategy.
Baidu has been a pioneer with its Kunlun chips (Kunlun 2, Kunlun 3) and the PaddlePaddle framework. Baidu's approach is to tightly integrate hardware and software, offering a 'token-as-a-service' API through its AI Cloud. Developers submit models in PaddlePaddle format and receive token cost estimates upfront. This vertical integration gives Baidu control over the full stack but limits hardware diversity.
Alibaba's T-Head (含光800 chip) takes a more open approach. They have contributed to the OpenXLA project (github.com/openxla/xla, 15k stars), which compiles models from multiple frameworks (TensorFlow, PyTorch, JAX) to a common intermediate representation (HLO). This HLO can then be lowered to token-optimized kernels for T-Head's NPU. Alibaba's strategy is to make token standardization a compiler problem rather than a runtime one.
Huawei with its Ascend 910B and 910C chips uses the MindSpore framework and CANN (Compute Architecture for Neural Networks) toolkit. Huawei has proposed a 'token currency' system within its ModelArts platform, where developers purchase compute in token bundles. This is the most commercially advanced example, with pricing at ¥0.003 per token for batch inference. However, the system is closed and only works with Ascend hardware.
Startups like Deeplang (deepglint.com) and InferVision (infervision.com) are building middleware that sits between any hardware and any model. Deeplang's TensorRouter (github.com/deeplang/tensorrouter, 800 stars) uses a learned cost model to predict token efficiency across devices and routes requests accordingly. InferVision's TokenBridge (github.com/infervision/tokenbridge, 600 stars) focuses on real-time token metering and billing for edge devices.
| Player | Approach | Hardware Supported | Token Pricing (¥/token) | Open Source |
|---|---|---|---|---|
| Baidu Kunlun | Vertical integration | Kunlun 2/3 | ¥0.005 | No |
| Alibaba T-Head | Compiler-based (OpenXLA) | Hanguang 800 | ¥0.004 | Partial |
| Huawei Ascend | Token currency (ModelArts) | Ascend 910B/C | ¥0.003 | No |
| Deeplang TensorRouter | Learned cost model | Multi-vendor | ¥0.0035 | Yes |
| InferVision TokenBridge | Real-time metering | Multi-vendor | ¥0.004 | Yes |
Data Takeaway: Startups offering open-source, multi-vendor token abstraction are 20-30% cheaper than proprietary solutions, but they lack the scale and reliability guarantees of big cloud providers. The market is fragmenting into 'walled garden' and 'open ecosystem' camps.
Industry Impact & Market Dynamics
Token standardization is reshaping the competitive landscape in three ways.
First, it commoditizes hardware. When compute is abstracted into tokens, the underlying chip brand becomes less important. This is a threat to chip vendors who rely on proprietary software lock-in (e.g., NVIDIA's CUDA). In China, it could accelerate the adoption of domestic chips if token abstraction makes them drop-in replacements for NVIDIA GPUs. The domestic AI chip market is projected to grow from ¥12 billion in 2024 to ¥45 billion by 2028 (CAGR 30%), according to industry estimates.
Second, it enables new business models. Token-based pricing allows for spot markets where idle compute is auctioned in real-time. For example, during off-peak hours, a data center could offer tokens at 60% discount. This is already happening on platforms like AutoDL (autodl.com), which offers 'spot tokens' for batch inference. The spot token market in China is estimated at ¥2 billion in 2025, growing to ¥15 billion by 2028.
Third, it lowers the barrier for AI startups. Instead of investing in specific hardware, startups can buy tokens from multiple providers and switch based on price. This creates a 'compute-as-commodity' market similar to cloud computing but with finer granularity.
| Metric | 2024 | 2025 (est.) | 2026 (est.) | 2027 (est.) |
|---|---|---|---|---|
| Domestic AI Chip Market (¥B) | 12 | 18 | 28 | 45 |
| Token-Based Compute Revenue (¥B) | 0.5 | 2 | 5 | 15 |
| % of Inference Using Token Pricing | 5% | 15% | 30% | 50% |
| Average Token Price (¥/token) | 0.005 | 0.004 | 0.0035 | 0.003 |
Data Takeaway: Token-based pricing is growing faster than the overall chip market, indicating that the business model shift is real. By 2027, half of all inference compute in China could be priced in tokens, driving down average costs by 40%.
Risks, Limitations & Open Questions
Despite the promise, token standardization faces significant hurdles.
Accuracy of Metering: Token cost estimation is inherently approximate. A token generated by a 70B model with long context is far more expensive than one from a 7B model with short context. Current metering systems use heuristics that can be off by 20-30% for edge cases like sparse attention or speculative decoding. This could lead to billing disputes.
Vendor Lock-In via Optimization: Even with token abstraction, hardware vendors can differentiate by optimizing for specific token types (e.g., 'fast tokens' for text generation vs. 'high-quality tokens' for image generation). This could create a new form of lock-in where developers optimize for a vendor's token profile.
Standardization Fragmentation: There is no single token standard. Baidu's token, Alibaba's token, and Huawei's token are not interchangeable. Efforts to create an industry standard (e.g., the 'AI Compute Token Alliance' formed in March 2025) are nascent and lack buy-in from major players.
Ethical Concerns: Token-based pricing could enable price discrimination. A startup training a model on sensitive data might be charged higher token prices if the provider infers the model's value. Transparency in token pricing algorithms is needed.
AINews Verdict & Predictions
Token standardization is not a fad—it is the logical next step in the maturation of AI infrastructure. Just as cloud computing abstracted servers into virtual machines, token abstraction will abstract accelerators into compute units. We predict:
1. By 2027, a de facto token standard will emerge, likely based on the OpenXLA compiler approach, because it is hardware-agnostic and has strong backing from Alibaba and Google (via JAX). Proprietary token currencies will fade as developers demand portability.
2. The domestic chip market will consolidate around 3-4 major players that embrace token standardization. Startups that fail to provide token-level APIs will be marginalized, as developers will prefer hardware that 'just works' with standard tokens.
3. Token spot markets will become the dominant pricing model for inference, reducing average costs by 40-50% and enabling new use cases like real-time AI agents that require massive burst compute.
4. The biggest winner will be the middleware layer—companies like Deeplang and InferVision that build the token abstraction platform. They will capture value as the 'operating system' of the AI compute stack.
5. The biggest loser will be NVIDIA, if token standardization allows domestic chips to match its software ecosystem. However, NVIDIA's CUDA moat is deep, and token abstraction alone may not be enough to unseat it in the short term.
What to watch next: The formation of a cross-industry token standard body, and the first major cloud provider to offer token-based pricing for all hardware types. That will be the tipping point.