An unverified X thread reports that DeepSeek is preparing an imminent V5 launch, with a rumored scale of 2 trillion parameters and training conducted fully on Huawei Ascend chips. The thread also says DeepSeek is reportedly keeping its open-weight strategy. It does not establish that the model has launched or provide benchmark results showing how it performs.
In a post on X, @Priyannkaaaa uses the headline “DeepSeek V5 Leak: Beats Astra” to frame the report as a possible performance breakthrough. But the post does not identify which Astra model is being compared, provide a benchmark or explain a testing method. The headline is therefore not a demonstrated performance result.
Image credit: @Priyannkaaaa on X
What the DeepSeek V5 report says
The thread describes four main possibilities:
DeepSeek is reportedly preparing a V5 release soon.
The model is rumored to have about 2 trillion parameters, rather than 3 trillion.
It would reportedly be the first DeepSeek model trained fully on Huawei Ascend accelerators instead of Nvidia hardware.
DeepSeek is reportedly keeping its open-weight strategy.
A parameter count is a rough description of a model’s scale, but it does not by itself establish how capable, efficient or useful the model will be. The post gives no architecture details, training-data information, release terms or other specifications that would help interpret the reported number.
The thread also attributes to DeepSeek founder Liang Wenfeng the description of V5 as the company’s “biggest bet yet.” The post presents that description as part of the report rather than as a separately documented official announcement.
The reported Huawei Ascend hardware tradeoff
The most specific hardware detail is the report that DeepSeek V5 would be trained fully on Huawei Ascend chips. The post says the model would need roughly four times as many Ascend accelerators as the equivalent Nvidia setup to reach the same training scale.
If accurate, that would describe a reported hardware tradeoff, not evidence that one platform produces a better model. The number of accelerators alone does not show the total cost, training speed or efficiency of either setup. The thread does not identify the chip models or provide measurements that would establish how the comparison was calculated.
The thread’s replies reflect that uncertainty. Several responses call for the actual model, real results or official confirmation before drawing conclusions. Those reactions do not verify or disprove the hardware report; they reinforce that the report has not yet been accompanied by the evidence needed to settle it.
Why the open-weight detail matters
The thread says DeepSeek is reportedly keeping its open-weight strategy. Open weights generally mean that a model’s trained parameters are made available for others to download or use under stated terms, although the exact availability and licence conditions matter. The report does not say when weights would be released, under what licence or whether all of the reported model’s capabilities would be available to the public.
The open-weight detail is therefore a reported part of the possible release strategy, not confirmation that readers can download or run DeepSeek V5 today.
What remains unconfirmed
The thread does not establish that DeepSeek V5 has launched. It provides no firm release date, pricing, availability details or complete model specification. It also includes no benchmark results, demonstrations or published weights.
The rumored 2-trillion-parameter figure remains unsupported by evidence in the report. The same is true of the Huawei Ascend training detail and the estimate that four times as many accelerators would be required. Those details may be important if confirmed, but they should not be treated as measured results yet.
The Astra comparison is also unresolved. The source does not define the Astra model, identify the tasks being compared or report scores from a controlled evaluation. Until those details and a real DeepSeek V5 release are available, the strongest supported conclusion is limited: the X thread reports a possible large DeepSeek model, a shift in training hardware and a continuing open-weight strategy, but it does not confirm the launch or prove the model’s performance.
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