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As I wrote in another comment, I think so for a few reasons:

1. The z.ai blog post says GML-5 is compatible with Ascends for inference, without mentioning training -- it says they support "deploying GLM-5 on non-NVIDIA chips, including Huawei Ascend, Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, and Hygon" -- many different domestic chips. Note "deploying". https://z.ai/blog/glm-5

2. The SCMP piece you linked just says: "Huawei’s Ascend chips have proven effective at training smaller models like Zhipu’s GLM-Image, but their efficacy for training the company’s flagship series of large language models, such as the next-generation GLM-5, was still to be determined, according to a person familiar with the matter."

3. You're right that z.ai trained a small image model on Ascends. They made a big fuss about it too. If they had trained GLM-5 with Ascends, they likely would've shouted it from the rooftops. https://www.theregister.com/2026/01/15/zhipu_glm_image_huawe...

4. Ascends just aren't that good

I think so for a few reasons:

1. The Reuters article does explicitly say the model is compatible with domestic chips for inference, without mentioning training. I agree that the Reuters passage is a bit confusing, but I think they mean it was developed to be compatible with Ascends (and other chips) for inference, after it had been trained.

2. The z.ai blog post says it's compatible with Ascends for inference, without mentioning training, consistent with the Reuters report https://z.ai/blog/glm-5

3. When z.ai trained a small image model on Ascends, they made a big fuss about it. If they had trained GLM-5 with Ascends, they likely would've shouted it from the rooftops.

4. Ascends just aren't that good

Also, you can definitely train a model on one chip and then support inference on other chips; the official z.ai blog post says GLM-5 supports "deploying GLM-5 on non-NVIDIA chips, including Huawei Ascend, Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, and Hygon" -- many different domestic chips. Note "deploying".

I've only seen information suggesting that you can run inference with Ascends, which is obviously a very different thing. The source you link also just says: "The latest model was developed using domestically manufactured chips for inference, including Huawei's flagship Ascend chip and products from leading industry players such as Moore Threads, Cambricon and Kunlunxin, according to the statement."

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    from collections import defaultdict
    
    def find_cheryls_birthday(possible_dates):
        # Parse the dates into month and day
        dates = [date.split() for date in possible_dates]
        months = [month for month, day in dates]
        days = [day for month, day in dates]
    
        # Step 1: Albert knows the month and says he doesn't know the birthday
        # and that Bernard doesn't know either. This implies the month has no unique days.
        month_counts = defaultdict(int)
        day_counts = defaultdict(int)
        for month, day in dates:
            month_counts[month] += 1
            day_counts[day] += 1
    
        # Months with all days appearing more than once
        possible_months = [month for month in month_counts if all(day_counts[day] > 1 for m, day in dates if m == month)]
        filtered_dates = [date for date in dates if date[0] in possible_months]
    
        # Step 2: Bernard knows the day and now knows the birthday
        # This means the day is unique in the filtered dates
        filtered_days = defaultdict(int)
        for month, day in filtered_dates:
            filtered_days[day] += 1
        possible_days = [day for day in filtered_days if filtered_days[day] == 1]
        filtered_dates = [date for date in filtered_dates if date[1] in possible_days]
    
        # Step 3: Albert now knows the birthday, so the month must be unique in remaining dates
        possible_months = defaultdict(int)
        for month, day in filtered_dates:
            possible_months[month] += 1
        final_dates = [date for date in filtered_dates if possible_months[date[0]] == 1]
    
        # Convert back to original format
        return ' '.join(final_dates[0]) if final_dates else "No unique solution found."
    
    # Example usage:
    possible_dates = [
        "May 15", "May 16", "May 19",
        "June 17", "June 18",
        "July 14", "July 16",
        "August 14", "August 15", "August 17"
    ]
    
    birthday = find_cheryls_birthday(possible_dates)
    print(f"Cheryl's Birthday is on {birthday}.")