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Huawei may be able to reduce China’s reliance on foreign high-bandwidth memory (HBM), but it has not demonstrated that it can replace foreign HBM suppliers or build a fully domestic AI-computing stack. The company’s approach combines inference software that uses memory more efficiently, storage and memory-management technologies, and a longer-term plan to integrate Huawei-controlled memory into future Ascend processors.
Those are meaningful steps toward supply-chain resilience. They are not the same as manufacturing competitive HBM at commercial scale. For investors and policymakers, the key distinction is whether Huawei is using less HBM, substituting slower memory for HBM, or producing an equivalent domestic product.
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What Huawei actually announced
Huawei’s reported strategy consists of several related but separate technologies. They should not be treated as one breakthrough.
| Technology | Primary purpose | What it may reduce | What it does not prove |
|---|---|---|---|
| Inference-optimization software | Improve data movement and memory utilization during AI inference | HBM required per workload or server | Domestic HBM manufacturing |
| AI SSD and storage-based memory management | Move selected model data or cache functions to lower memory tiers | Pressure on scarce HBM capacity | HBM-equivalent latency or bandwidth |
| HiBL 1.0 | Huawei’s proposed in-house memory technology for future Ascend products | Direct reliance on foreign HBM suppliers, if production succeeds | Commercial-scale output, yields, or performance parity |
| Ascend system architecture | Combine accelerators, memory, networking, and software | Reliance on Nvidia’s complete platform | Drop-in parity across AI workloads |
In August 2025, Huawei was reported to have announced a software tool designed to accelerate AI inference while reducing reliance on HBM. The company reportedly planned to open-source the tool in September 2025 through its developer community and later more broadly. That was an announced plan; the existence of a plan should not be confused with a verified public release, license, or independently reproducible benchmark. South China Morning Post reporting described the tool’s purpose, but publicly available independent technical evidence remains limited.
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Huawei has also promoted storage-oriented approaches that can move some data outside the fastest memory tier. These methods address a different bottleneck from domestic HBM production. They may help a system operate with less HBM, but data stored in conventional DRAM or on an SSD generally carries a latency, bandwidth, power, or concurrency penalty.
Why HBM matters to AI accelerators
High-bandwidth memory is a stacked memory technology placed close to an AI processor, usually through advanced packaging. AI accelerators repeatedly move model weights, activations, and intermediate results. HBM helps supply that data at very high bandwidth while keeping the memory physically close to the processor.
The important attributes are not capacity alone. An AI system also depends on:
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- Latency: how quickly a memory request is answered.
- Power efficiency: how much energy data movement consumes.
- Packaging proximity: how effectively memory and processor can communicate.
- Thermal and electrical reliability: whether the system can run continuously.
- Yield and volume: whether enough usable stacks can be produced at an acceptable cost.
HBM supply is concentrated among SK Hynix, Samsung, and Micron. That concentration creates a strategic vulnerability for Chinese AI-chip designers, especially as export controls and restricted access to leading AI hardware increase the value of every unit of high-speed memory.
But reducing HBM consumption is not the same as replacing HBM. A system can conserve HBM by reusing data, compressing it, or moving less frequently used information to slower tiers. That may be a valuable engineering solution, while still leaving HBM essential for the most demanding parts of the workload.
How software can reduce memory pressure
Huawei has not publicly provided enough detail to establish exactly which techniques its announced tool uses. At a systems level, an inference optimizer could reduce memory requirements through several methods:
- Keeping frequently reused data in fast memory.
- Reducing unnecessary transfers between the accelerator and memory.
- Compressing or quantizing model data.
- Placing infrequently used parameters in conventional DRAM or storage.
- Scheduling computation around memory availability.
- Exploiting sparsity in models, including mixture-of-experts architectures.
- Reusing cached results rather than recalculating or reloading them.
The practical result could be a smaller HBM footprint per deployed model. It does not create additional HBM capacity, and it does not make an SSD perform like HBM.
The benefits will probably vary by workload. Small models may not be HBM-limited. Large mixture-of-experts models may benefit from intelligent placement because only some experts are active for each request. Multimodal models can generate irregular memory traffic that is more difficult to optimize. A technique that works in batch inference may also perform poorly in interactive, low-latency serving.
Three different meanings of “less dependent”
Claims about reducing foreign-memory dependence become clearer when separated into stages:
- Lower consumption: fewer foreign HBM chips are required for a given AI workload.
- Supplier diversification: Chinese buyers can source memory from domestic and foreign suppliers.
- Domestic substitution: Chinese-made HBM meets required performance, reliability, yield, and volume targets.
- Full ecosystem independence: China can design, fabricate, package, test, deploy, and maintain AI systems without critical foreign inputs.
Huawei’s software announcement potentially advances the first stage. Its in-house HBM roadmap is aimed at the third. Neither demonstrates the fourth.
Huawei’s Ascend roadmap and HiBL
Huawei’s Ascend family is intended to provide an alternative to Nvidia’s AI accelerators. The Ascend 910C was identified as Huawei’s current commercial chip in reporting on the company’s 2025 roadmap.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The reported roadmap included the Ascend 950PR and 950DT for early 2026, the Ascend 960 for 2027, and the Ascend 970 for 2028. Huawei said the Ascend 950PR would integrate its HiBL 1.0 memory technology. Reported specifications included 128 GB of memory and 1.6 TB/s of bandwidth. Those figures are Huawei-reported roadmap specifications, not independently validated production benchmarks. See the reporting from Investing.com and Tom’s Hardware.
A roadmap can show strategic direction, but it does not establish that a product has shipped in volume. The decisive questions are whether Huawei can manufacture the memory consistently, obtain acceptable yields, package it reliably with the accelerator, and deploy it in systems that deliver sustained performance under real workloads.
Can China’s memory industry support the plan?
China’s domestic semiconductor effort covers much more than Huawei. Semiconductor Manufacturing International Corporation, or SMIC, is relevant to logic production. ChangXin Memory Technologies, or CXMT, is strategically important to domestic DRAM supply. Yangtze Memory Technologies, or YMTC, is a major Chinese NAND producer and has pursued methods for stacking memory layers with less advanced equipment.
These companies are relevant to a broader domestic supply chain, but their involvement should not be interpreted as proof that China already has high-volume, competitive HBM. HBM requires advanced DRAM production, stacking, interconnection, packaging, testing, and reliable operation at scale.
There is also no guarantee that domestic means cheaper. Reuters reporting cited by Investing.com said CXMT had raised prices for Huawei and that comparable Chinese DDR5 server memory could cost more than Samsung’s reported price of about $1,240 for a 64 GB module. That suggests China may achieve supply security before it achieves cost leadership. A domestically controlled component can be strategically valuable even when it is more expensive than an imported alternative.
Domestic-equipment substitution is also partial and evolving. A system can be described as domestic at the chip level while still depending on foreign semiconductor equipment, materials, packaging tools, software, or components elsewhere in the production chain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The software stack is another bottleneck
Replacing Nvidia is not simply a matter of producing an accelerator with sufficient theoretical bandwidth. Developers must port models and kernels, support operators, adapt compilers, manage distributed communication, debug performance problems, and maintain deployment tools.
Huawei’s CANN software ecosystem is intended to support Ascend processors, but migration from Nvidia’s CUDA ecosystem can be a multiyear engineering project. A company may have compatible hardware and still face lower productivity, missing operators, immature compiler paths, or inconsistent performance across models. The Center for Strategic and International Studies analysis identified software and ecosystem migration as a major part of China’s AI-computing challenge.
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A 2026 field study of large-model workloads on Huawei Ascend systems documented failures and hard limits involving the accelerator, compiler, operator library, and inference plug-in. It did not specifically test Huawei’s HBM-reduction tool, so it is not evidence against that tool. It is, however, relevant evidence that system integration and software maturity remain material constraints. The study is available on arXiv.
What investors should watch next
The claim becomes substantially stronger if Huawei or its partners disclose evidence in four categories.
Hardware evidence
- Volume production of domestic HBM.
- Verified generation, interface, and stack specifications.
- Yield rates and production capacity.
- Packaging quality, error rates, and long-term reliability.
- Sustained bandwidth under representative AI workloads.
- Commercial availability in Ascend systems.
Software evidence
- A public release, documentation, and license for the optimization tool.
- Reproducible memory-reduction results against a clearly defined baseline.
- End-to-end tokens-per-second measurements.
- Latency at different batch sizes and concurrency levels.
- Results on dense, sparse, multimodal, and mixture-of-experts models.
- Performance measurements showing what happens when data spills into DRAM or SSD storage.
Commercial evidence
- Deployment in Chinese data centers.
- Customer references and long-term supply agreements.
- System shipment volumes.
- Acceptable cost per inference, not just lower HBM use.
- Evidence that imports of HBM are falling because domestic supply is replacing them, rather than because deployments are simply being delayed or downsized.
The trade-offs behind the workaround
Using less HBM can be valuable, but the bottleneck may move elsewhere. More traffic to ordinary DRAM can increase latency. More SSD traffic can require additional bandwidth, caching, networking, and power. A workload that performs well for one inference stream may degrade sharply when many users access the system concurrently.
There is also a cost in engineering time. Hardware-specific optimization can improve performance on Huawei’s platform while increasing dependence on Huawei’s software stack. That may strengthen China’s domestic ecosystem, but it can reduce portability to Nvidia, AMD, or other systems.
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Bottom line: a bridge, not proof of independence
Huawei’s strategy is credible as a way to stretch scarce memory supplies. Better inference scheduling and storage-aware memory management could reduce the amount of foreign HBM needed per workload. That would matter for Chinese AI deployment under supply and export constraints.
It is not evidence that Huawei has solved China’s HBM shortage, eliminated foreign-memory dependence, or matched the leading HBM suppliers. The HiBL 1.0 roadmap is a more direct attempt to address the hardware problem, but its reported capacity and bandwidth remain company claims until independently verified in commercial systems.
The most defensible interpretation is that Huawei is building a bridge: software can reduce near-term memory pressure while the company and China’s wider semiconductor industry work toward domestic HBM, advanced packaging, and a more mature Ascend software ecosystem. China may gain resilience before it gains parity, and supply-chain control before it gains lower costs.
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