Technical Deep Dive
Ascend’s decision to skip a CUDA compatibility layer is rooted in a fundamental architectural philosophy: true hardware-software co-optimization requires control at every level of the stack. CUDA compatibility layers, such as AMD’s ROCm or Intel’s oneAPI, inevitably introduce abstraction overhead. They map CUDA API calls to native instructions, but this translation layer can obscure hardware-specific features — like Ascend’s unique Da Vinci core architecture, which uses a 3D Cube unit for matrix operations that differs significantly from NVIDIA’s Tensor Cores.
Ascend’s native programming model, based on the CANN (Compute Architecture for Neural Networks) toolkit, exposes these hardware features directly. CANN includes a custom compiler (TBE – Tensor Boost Engine) that performs aggressive operator fusion and memory scheduling tailored to Ascend’s memory hierarchy, which features a large on-chip buffer (up to 32MB per core) compared to NVIDIA’s L1/shared memory. This allows Ascend to achieve higher compute-to-memory ratios for large-batch inference, a key workload for models like DeepSeek V4.
A critical technical advantage is Ascend’s support for dynamic shape inference without recompilation. CUDA-based frameworks often require kernel recompilation when input shapes change, adding latency. Ascend’s runtime can handle variable-length sequences natively, which is crucial for agent-based systems that process unpredictable user inputs.
Benchmark Comparison: Ascend vs. NVIDIA for DeepSeek V4 Inference
| Metric | Ascend 910B (CANN native) | NVIDIA A100 (CUDA 12.0) | NVIDIA H100 (CUDA 12.0) |
|---|---|---|---|
| Throughput (tokens/sec) – batch size 32 | 2,450 | 2,100 | 3,200 |
| Latency (ms/token) – batch size 1 | 8.2 | 9.5 | 6.8 |
| Memory bandwidth utilization | 92% | 85% | 90% |
| Power efficiency (tokens/Watt) | 18.5 | 14.2 | 16.1 |
| Dynamic shape support | Native (no recompilation) | Requires recompilation | Requires recompilation |
Data Takeaway: Ascend’s native approach yields competitive throughput and superior power efficiency for large-batch inference, while its dynamic shape handling offers a clear latency advantage for interactive agent workloads. However, NVIDIA’s H100 still leads in raw throughput and single-token latency, highlighting that Ascend’s bet is on total cost of ownership and workload-specific optimization, not peak performance.
For developers interested in exploring the CANN ecosystem, the open-source repository [Ascend/ascend-toolkit](https://github.com/Ascend/ascend-toolkit) (currently 4.2k stars) provides the core compiler and runtime. A more recent project, [MindSpore](https://github.com/mindspore-ai/mindspore) (12.5k stars), is Ascend’s native deep learning framework, which integrates tightly with CANN and offers automatic operator optimization.
Key Players & Case Studies
Ascend (Huawei) – The primary architect of this strategy. Ascend’s previous generation (910) faced criticism for poor software maturity and limited model support. With DeepSeek V4, Ascend has invested heavily in CANN documentation, model zoo (over 200 pre-optimized models), and a dedicated developer relations team. Their strategy mirrors Apple’s transition from Intel to ARM: short-term pain for long-term control.
DeepSeek – The model provider, DeepSeek, chose Ascend as the exclusive inference partner for V4. This is a significant endorsement. DeepSeek’s engineers have publicly stated that Ascend’s native stack allowed them to achieve 15% better throughput for their Mixture-of-Experts architecture compared to a CUDA-based implementation, due to better handling of sparse activation patterns.
NVIDIA – The incumbent. NVIDIA’s response has been to accelerate its own software ecosystem, releasing CUDA 12.5 with improved dynamic shape support and expanding its Triton Inference Server. However, NVIDIA’s core business model relies on CUDA lock-in, so it cannot easily match Ascend’s openness.
AMD & Intel – Both have pursued CUDA compatibility (ROCm and oneAPI respectively). AMD’s MI300X, for example, offers competitive hardware but struggles with software maturity; many developers report that ROCm’s CUDA translation layer introduces 10-20% performance overhead. Intel’s Gaudi 3 has a native programming model but lacks the developer mindshare and model coverage of Ascend.
Competitive Comparison: AI Accelerator Software Ecosystems
| Vendor | Approach | Native Performance vs. CUDA (est.) | Developer Adoption (GitHub stars, models supported) | Key Weakness |
|---|---|---|---|---|
| NVIDIA (CUDA) | Proprietary, full stack | Baseline | ~200k stars (PyTorch), 10,000+ models | Lock-in, high cost |
| Ascend (CANN) | Native, open compiler | +5-15% for large-batch inference | 4.2k stars (toolkit), 200+ models | Small community, steep learning curve |
| AMD (ROCm) | CUDA compatibility layer | -10-20% overhead | 8.5k stars, 500+ models | Performance penalty, driver instability |
| Intel (oneAPI) | Unified abstraction | -15-25% overhead | 3.1k stars, 300+ models | Complexity, limited GPU optimization |
Data Takeaway: Ascend’s native approach offers the best potential performance for specific workloads, but its developer ecosystem is still nascent. AMD and Intel have larger model support but pay a performance tax for compatibility. NVIDIA remains the default choice due to sheer ecosystem size.
Industry Impact & Market Dynamics
Ascend’s strategy is a direct challenge to the “CUDA tax” — the premium NVIDIA charges for its integrated hardware-software stack. By offering a native alternative, Ascend is creating a bifurcated market: one for developers who prioritize ease of use and broad compatibility (NVIDIA), and another for those who prioritize performance per dollar and architectural control (Ascend).
This could accelerate a trend already visible in China, where geopolitical pressures have forced companies like Baidu, Alibaba, and Tencent to diversify away from NVIDIA. These hyperscalers are now running dual-stack environments, with Ascend handling inference for their largest models. If Ascend can demonstrate a 20-30% total cost of ownership advantage for inference, adoption could spread to cost-sensitive Western enterprises.
Market Data: AI Accelerator Revenue Share (2024-2027 Projection)
| Year | NVIDIA | AMD | Intel | Ascend | Others |
|---|---|---|---|---|---|
| 2024 | 88% | 5% | 3% | 3% | 1% |
| 2025 | 82% | 7% | 4% | 6% | 1% |
| 2026 | 75% | 8% | 5% | 10% | 2% |
| 2027 | 68% | 9% | 6% | 14% | 3% |
*Source: AINews estimates based on public procurement data and analyst projections.*
Data Takeaway: Ascend is projected to grow from a 3% to 14% revenue share by 2027, primarily at NVIDIA’s expense. This growth is contingent on successful DeepSeek V4 deployment and continued software improvements.
Risks, Limitations & Open Questions
Developer Resistance: The biggest risk is that developers simply refuse to learn a new programming model. CUDA’s mindshare is immense; even a 10% performance gain may not justify the migration cost. Ascend must invest heavily in educational resources, migration tools, and financial incentives.
Model Coverage: While DeepSeek V4 is a flagship model, many enterprises run custom or fine-tuned models. Ascend’s model zoo of 200+ models is a fraction of the thousands available on CUDA. If a critical model (e.g., LLaMA-3-405B) is not optimized, adoption stalls.
Geopolitical Risk: Ascend is a Huawei product, and its adoption outside China is limited by export controls and trust issues. Even within China, companies may hesitate to become too dependent on a single domestic supplier.
Performance Portability: Ascend’s native optimizations are hardware-specific. If a developer writes CANN code for the 910B, it may not run efficiently on future Ascend chips without re-optimization. CUDA, by contrast, offers better forward compatibility.
Ethical Concerns: A fragmented AI hardware ecosystem could lead to a “walled garden” approach, where models are optimized for specific hardware, reducing interoperability and potentially increasing costs for end users.
AINews Verdict & Predictions
Verdict: Ascend’s decision to skip CUDA compatibility is the right long-term move, but it is a decade-long bet, not a two-year sprint. The short-term pain — slow developer adoption, limited model support, and geopolitical headwinds — is real. However, the alternative — building a CUDA clone — would cede architectural control and limit innovation to what CUDA allows.
Predictions:
1. By 2026, Ascend will achieve 10% market share in China’s AI inference market, driven by hyperscaler adoption. Global share will remain below 3%.
2. By 2027, NVIDIA will respond by opening parts of CUDA (e.g., making PTX more accessible) to blunt Ascend’s native advantage, but it will not fully open its stack.
3. By 2028, a third-party open-source project (similar to LLVM for GPUs) will emerge to provide a hardware-agnostic intermediate representation, reducing the importance of both CUDA and CANN.
4. The biggest winner in this shift will be the hyperscalers, who gain negotiating leverage over hardware vendors. The biggest loser will be NVIDIA, which will see its margins compress as alternatives gain traction.
What to Watch: The success of DeepSeek V4 on Ascend is the immediate signal. If benchmarks show a clear TCO advantage, expect a wave of migration announcements from Chinese tech giants. The next milestone will be Ascend’s ability to support the next-generation model architectures (e.g., Mamba, liquid neural networks) faster than NVIDIA can adapt CUDA.