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There is no evidence here for a universal winner. NVIDIA offers a mature, CUDA-centered GPU platform with extensive published benchmark material. Huawei’s Ascend and Atlas systems are a substantial Chinese alternative, including a reported live-network deployment, but the available figures are not a controlled NVIDIA-versus-Ascend comparison. For a real procurement decision, measure the complete serving platform—model, precision, concurrency, latency target, software, networking, storage, power and operating cost—not the accelerator name alone.
What the comparison can—and cannot—tell you
Inference performance depends on the entire path from request arrival to generated token. A device with strong peak compute can lose in production if its kernels, memory capacity, interconnect, scheduler or model implementation are a poor fit. Conversely, a platform with less familiar hardware may be competitive on a specific model after substantial software tuning.
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The evidence currently available supports three conclusions:
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- NVIDIA has a broad CUDA software ecosystem and publishes results through its developer benchmark material and MLPerf participation. Those results are tied to particular hardware, models, precision settings and software versions.
- Huawei’s Ascend portfolio spans modules, boards, servers and supernodes built around Ascend processors and foundational software. Huawei and China Mobile reported a live-network validation of an Ascend system for long-context inference.
- No source reviewed establishes equal-model, equal-precision, equal-concurrency and equal-latency measurements for NVIDIA and Ascend. Therefore, neither platform can be declared faster or cheaper in general.
How the two platform families are structured
NVIDIA: a GPU platform centered on CUDA
NVIDIA’s inference offering is more than a GPU card. CUDA provides the programming and runtime base used by widely deployed frameworks, optimized libraries and serving systems. Published NVIDIA and MLPerf results normally identify the accelerator, model, numerical precision, batch or concurrency, software stack and system configuration. That detail is useful because it shows how sensitive a result is to tuning, but it also means a headline number should not be transferred to a different GPU, model or service objective.
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For an engineering team already using CUDA kernels, CUDA-specific operators or NVIDIA-optimized libraries, staying on NVIDIA can avoid a substantial porting project. The benefit is not simply raw arithmetic throughput; it is the availability of tested implementations, profilers, deployment examples and staff experience.
How to read NVIDIA benchmark claims
NVIDIA’s developer benchmark pages and the MLPerf overview provide useful reference points, but they are not a single, universal scorecard. A reported result is meaningful only with its hardware, model, precision, batch or concurrency, serving software and system boundary. A number from one NVIDIA configuration cannot be treated as a result for every NVIDIA GPU, just as a result from a tuned multi-device system cannot be compared with a single-device test. Use these publications to identify reproducible configurations and measurement practices, then repeat the workload on the systems under consideration.
Huawei Ascend and Atlas: a Chinese accelerator and system portfolio
Huawei describes Atlas as a family of AI-computing products based on Ascend processors and its foundational software. The portfolio includes modules, boards, servers and supernodes for both training and inference. This system-level range matters: an inference node assembled from several accelerators, high-bandwidth links, storage and a scheduler behaves differently from a single accelerator in a laboratory test.
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Why the system boundary matters
Atlas terminology can refer to a module, board, server or supernode, while an NVIDIA result may refer to a GPU, a server or a larger cluster. Those are different units of comparison. Document the number of accelerators, host memory, interconnect, storage path, cache layer and scheduler in every test. Otherwise, an apparent accelerator advantage may actually be a networking, memory or software-configuration advantage.
What the published Huawei result actually shows
The reported 372% token-throughput improvement
In a June 24, 2026 corporate announcement, Huawei attributed the following result to an AI Inference Acceleration Solution validated with China Mobile Hubei: “The solution delivers up to a 372% improvement in token throughput for long-sequence artificial intelligence (AI) inference workloads.”
The announcement describes an Ascend A3 SuperPoD, OceanStor A800 storage, Unified Cache Manager and vLLM-Ascend operating in a live-network validation. Inputs were simulated from 8K to 190K tokens, and the named models included MiniMax M2.5 and GLM-5.1. “Up to” is important: it identifies the best reported case within Huawei’s stated test scope, not a guaranteed gain for every model or traffic pattern.
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What that percentage does not tell you
- It does not establish the absolute tokens-per-second rate or the latency percentile users experienced.
- It does not identify an equivalent NVIDIA server, software release or power measurement.
- It does not show whether every input length from 8K through 190K achieved the maximum improvement.
- It does not establish equal output lengths, concurrency, precision, model quality or operating cost.
Those missing controls are why the figure is useful evidence that Huawei has optimized a particular long-context solution, but not a substitute for a matched benchmark.
Additional Ascend deployment evidence
A July 2026 field study examined mixture-of-experts and multimodal serving on Ascend using CANN and vLLM-Ascend. It adds evidence that these workloads can be deployed outside a laboratory, but the material does not establish a controlled NVIDIA comparison. Treat it as deployment evidence, not as a ranking.
Software portability is a major performance and cost variable
Why a CUDA application is not automatically portable
A June 2025 report from the Mitsui Global Strategic Studies Institute states that CUDA cannot be used with non-NVIDIA GPUs and describes migration as requiring code porting and performance optimization. That assessment reflects the practical issue facing teams with CUDA-specific code: changing hardware can require replacing kernels, adapting memory and communication paths, validating numerical behavior and tuning the serving runtime again.
Portable framework code may move more easily than custom CUDA extensions, but “the model loads” is not the same as “the model meets the service-level objective.” Unsupported operators may fall back to slower implementations; fused kernels may be unavailable; quantization formats may differ; and a model’s attention, mixture-of-experts routing or multimodal preprocessing can expose gaps that are invisible in a small smoke test.
Questions to answer before selecting Ascend
- Does the exact framework version support your model architecture, tokenizer, attention implementation and generation features?
- Are every required operator and datatype implemented by the target CANN and vLLM-Ascend releases?
- Do the intended quantization formats preserve acceptable quality and throughput?
- Can your monitoring, tracing, autoscaling and security tooling run on the target operating environment?
- Who will maintain custom kernels and revalidate performance after firmware, driver or framework updates?
Answer these questions with a runnable build of the production checkpoint, not a vendor compatibility list alone.
How to run a defensible NVIDIA-versus-Ascend test
The following controls prevent a benchmark from becoming a comparison of unrelated demonstrations.
- Fix the model and checkpoint. Use the same model weights, tokenizer, prompt template and generation settings. Record the commit or release identifier.
- Define the request distribution. Specify input and output-length distributions, including long-context cases. A test with 8K inputs answers a different question from one reaching 190K.
- Set concurrency and batching explicitly. Record request arrival rate, active sequences, maximum batch size, continuous-batching policy and queue limits.
- Use equivalent numerical settings. Match FP16, BF16, FP8 or another precision where both platforms support it, and document any calibration or quantization method.
- Choose latency objectives before measuring. Report time to first token, inter-token latency and tail latency, not throughput alone. A system can produce many tokens while violating an interactive latency target.
- Freeze the serving software. Record framework, runtime, driver, firmware, compiler, kernel library and configuration. Test each platform with the best supported stack, but disclose platform-specific tuning.
- Describe the system boundary. State whether the result uses one accelerator, one server, several nodes or a supernode. Include CPU, memory, network fabric, storage and any cache layer.
- Warm up and repeat. Exclude initialization from steady-state measurements, then run enough repetitions to report variation and tail behavior. Keep the measurement period and thermal conditions consistent.
- Verify output quality. Compare generated outputs, numerical tolerances and task metrics. A faster configuration that changes model quality is not an equivalent result.
- Publish raw observations. Include throughput, latency percentiles, utilization, memory use, failures, queueing and power so another team can reproduce the conclusion.
Metrics that matter for inference
| Metric | What it tells you | Common mistake |
|---|---|---|
| Time to first token (TTFT) | How quickly an interactive request begins producing output; especially sensitive to prompt length and queueing. | Reporting only average latency and hiding slow first responses. |
| Inter-token latency | How smoothly tokens arrive after generation starts. | Calling high aggregate throughput “responsive” when token delivery is uneven. |
| Tokens per second | Generation capacity under a specified workload. | Comparing different sequence lengths, precisions or concurrency. |
| Tail latency | What the slowest 1% or 0.1% of users experience. | Using a mean that masks overload and queue buildup. |
| Requests per second | Service capacity for a defined request shape. | Ignoring output length, which changes the work per request. |
| Memory and cache use | Whether the model and key/value cache fit at the desired context and concurrency. | Testing a short prompt, then discovering long-context requests trigger eviction or out-of-memory errors. |
| Failure and recovery rate | Whether the service remains available during retries, restarts and capacity changes. | Counting only successful requests and excluding timeouts or failed batches. |
| Power at target load | The energy consumed while meeting the chosen throughput and latency objective. | Using a thermal-design maximum instead of measured production power. |
Questions to put in a vendor evaluation
Ask NVIDIA and Huawei for the same evidence package rather than accepting a headline number from either side.
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- Which model checkpoint, tokenizer, input and output lengths, precision and quantization method were used?
- What were TTFT, inter-token latency and tail-latency percentiles at the quoted throughput?
- Which versions of CUDA or CANN, the serving framework, drivers, firmware and kernel libraries were installed?
- How much platform-specific code, kernel work or tuning was required, and who owns it after deployment?
- What storage, cache and networking components are included in the quoted system?
- Are support, software updates, observability integrations and failure-recovery procedures included in the proposal?
Require reproducible configuration files and test logs where possible. A result that cannot be recreated is a lead for investigation, not a procurement guarantee.
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Cost per token: why no honest winner can be calculated here
A fair economic comparison needs more than an accelerator price. At minimum, calculate:
- Acquisition or rental cost for the complete server or cluster.
- Utilization at the target service level, including idle and failover capacity.
- Electricity, cooling and data-center overhead over the operating period.
- Software engineering for porting, kernel work, validation and ongoing upgrades.
- Networking, storage, support, observability and operations.
- Achieved tokens per second at the agreed latency and quality target.
A useful model is:
cost per token = (amortized infrastructure + energy and facility cost + software and operations cost) / production tokens served
The available sources do not provide a common system price, utilization, power measurement, operating period and workload for both platforms. Any numerical cost-per-token claim would therefore be speculative. A lower purchase price can disappear if utilization is poor or migration takes months; a higher-priced system can be economical if it sustains more compliant throughput with less engineering effort.
Calculate the numerator over the same period for both candidates and count only production-quality tokens: requests that meet the latency, availability and output-quality requirements. If one system needs extra capacity for failover or long-context memory, include that capacity rather than comparing nominal accelerator counts.
Choosing a platform by workload
When NVIDIA is the lower-risk choice
- Your application already depends on CUDA extensions or NVIDIA-specific libraries.
- You need broad support for popular frameworks, model-serving tools and optimization examples.
- You require an extensive body of published benchmark and operational experience.
- Your team cannot fund a porting and performance-tuning project.
These are risk and time-to-deployment advantages, not proof that NVIDIA wins every measured workload.
When an Ascend evaluation is justified
- Your procurement, supply-chain or regional policy favors a Chinese accelerator platform.
- Your target models have verified operator, precision and serving support in CANN and vLLM-Ascend.
- You can benchmark the exact production workload and staff the required porting and optimization.
- The available system configuration, support arrangement and data-center environment fit your deployment.
Huawei’s reported long-sequence validation makes Ascend particularly worth testing for services dominated by very large prompts, but it remains a vendor-specific result rather than a universal long-context advantage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common comparison failures and how to correct them
Comparing a GPU card with a complete supernode
Correct the scope by comparing equivalent system boundaries. A multi-accelerator node with high-bandwidth networking and storage should be evaluated against a similarly configured NVIDIA system, or the result should be labeled as a component test.
Using different models or precision
Correct it by pinning the same checkpoint and numerical format. If one platform requires a different precision, report the quality impact and treat the result as a workload-specific trade-off.
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Quoting a peak throughput number
Correct it by adding TTFT, inter-token and tail latency at the intended concurrency. Throughput without a service objective is not a deployment recommendation.
Assuming framework compatibility equals performance parity
Correct it with an operator-level audit and a production-shaped benchmark. Measure fallback kernels, compilation time, memory use and failure behavior, not just successful model loading.
Ignoring migration labor
Correct the business case by estimating porting, optimization, testing, documentation and future upgrade work. The Mitsui report specifically identifies code porting and performance optimization as migration costs when moving away from CUDA.
Changing the test after seeing a result
Correct this by writing the acceptance criteria first: model quality, maximum TTFT, inter-token target, tail percentile, concurrency and availability. Otherwise, a platform can appear to win simply because the workload or success definition was adjusted after measurement.
A practical evaluation plan for an enterprise team
Phase 1: establish a functional baseline
Run the unmodified production model on each supported stack. Confirm tokenizer behavior, output quality, tool calls, batching, streaming and failure recovery before optimizing anything.
Phase 2: tune each platform fairly
Allow platform specialists to use documented optimizations, including kernel fusion, continuous batching, graph compilation, quantization and parallelism. Keep a change log so a result is not presented as “out of the box” when it required extensive engineering.
Phase 3: test production traffic shapes
Replay representative prompt and completion lengths, concurrency spikes and long-context requests. Include the highest context lengths your product promises, not only the median case.
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Phase 4: add reliability and economics
Run soak tests, restart and upgrade scenarios, node failures, cache pressure and capacity changes. Measure power and operational labor at the same time as throughput and latency, then calculate cost per compliant token.
Phase 5: make a workload-specific decision
Select the platform that meets quality, latency, availability and security requirements at an acceptable total cost. Keep the raw test artifacts and software versions so the decision can be revisited when models or runtimes change.
Bottom line for buyers
NVIDIA is the safer default when CUDA compatibility, broad software support and established benchmark coverage dominate the decision. Huawei Ascend is a credible alternative to evaluate, especially where regional supply, policy or a verified long-context deployment matters. The reported “up to a 372%” improvement belongs to Huawei’s June 2026 China Mobile validation and cannot be used as a direct NVIDIA comparison. The only defensible answer to “which is faster or cheaper?” is the result of a controlled, production-shaped test on your model and service target.
Frequently Asked Questions
Does Huawei’s 372% figure prove Ascend is faster than NVIDIA?
No. Huawei attributed the “up to a 372%” token-throughput improvement to an Ascend A3 SuperPoD solution validated with China Mobile Hubei. The published description does not provide a matched NVIDIA system, workload, precision, latency target and cost, so it cannot establish a general performance ratio.
Can existing CUDA applications run unchanged on Ascend?
Do not assume that. A June 2025 Mitsui Global Strategic Studies Institute report states that CUDA cannot be used with non-NVIDIA GPUs and identifies code porting and performance optimization as migration costs. Framework-level portability may be easier than moving custom kernels and extensions.
Is Ascend suitable for long-context inference?
It is worth evaluating when long prompts dominate. Huawei reported a live-network validation using simulated 8K-to-190K-token inputs, MiniMax M2.5 and GLM-5.1, with Ascend A3 SuperPoD, OceanStor A800, Unified Cache Manager and vLLM-Ascend. That is deployment evidence for the stated configuration, not proof of universal advantage.
What is the fairest way to compare NVIDIA and Ascend?
Use the same checkpoint, tokenizer, prompt and completion distributions, precision, concurrency, latency objectives and measurement period. Record the serving software, system boundary, networking, storage, cache, power and output quality, then report throughput alongside TTFT, inter-token and tail latency.
Does MLPerf settle the NVIDIA-versus-Ascend question?
No. NVIDIA’s developer benchmark material and MLPerf provide benchmark context and configuration-specific results. They do not, by themselves, create equal tests for every NVIDIA and Ascend system. Check the hardware, model, precision, software and system configuration before drawing a comparison.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteHow should an enterprise calculate cost per token?
Include complete acquisition or rental cost, utilization, electricity, cooling, support, storage, networking, operations and software migration. Divide those costs by production tokens that meet the agreed quality, latency and availability targets. The cited material does not supply a common dataset for a numerical NVIDIA-versus-Ascend calculation.
What does the July 2026 Ascend field study add?
It provides additional deployment evidence for mixture-of-experts and multimodal serving with CANN and vLLM-Ascend. It does not, based on the material reviewed, establish a controlled NVIDIA comparison or a universal ranking.
The Bottom Line
NVIDIA remains the lower-risk choice for CUDA-dependent teams, while Huawei Ascend merits a controlled evaluation where policy, supply or long-context requirements make it relevant. No published figure here establishes a universal speed or cost winner. Compare complete, production-shaped systems and count migration and operating costs.
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