This doesn't really have anything to do with GLM-5.3. This is what happens when you share models between users and don't do your bookkeeping properly, the actual model doesn't matter. What it shows is that there isn't enough isolation between the user sessions and that memory can overlap when that should never be possible.
This is an SGLANG and a Cloudflare bug long before it is a Z.AI model bug, I don't see what they could have done about it. Cloudflare should own it instead of naming other (imo innocent) parties in the title.
I like this RCA a lot more than most. I appreciate the ownership and clear explanation of why their testing did not catch this bug.
Also impressed with the speed to address the issue unplug the model and then pretty quick remediation, the speed to remediation matters less to me, but good to see they can act quickly on their platform to kill the serving of the model.
"We deployed GLM 5.3 with a 1,048,576-token context window, but the KV cache pool on our prefill GPUs only had room for 681,280 tokens. We were aware of this mismatch before launch. "
Did they advertise it as 1,048,576 token when they only had room for 681,280 tokens?
Next sentence: "We expected the engine to return an error for any prompt it could not process, and our long-context tests returned successful responses, so we did not believe this to be an issue."
Which is pointing the finger at Sglang, who also weren't responsible for this.
At a minimum someone should have realized during testing that their utilization figures are off by 40%.
Doesn't matter, they could have marketed it as 1 million token window, you would then infer, get to 600k and get an error. You won't be able to finish your work, and you won't get credit for 600k tokens.
Bro, I had to read this sentence in the context of the previous one, 3 fcking times.. and it sill does not make any sense to me.
Sounds like they are saying they tested with a long prompt and just tested for a failure, but the test likely would have seen that same KV cache leakage and just interpreted it as success.
Other providers probably don't even know/care that this is happening
Last year it happened to me more than once that dictating something in chatgpt would become a completely different message after processing, not in a misheard type of way but just a separate conversation more easily explained by the same type of KV cache issue here.
I think in the last year I've had it happen once where I seemingly got someone else's response, it was very odd. I rewound and got what I expected. It was correlated to Anthropic's roll out of new models at the time which always seems to cause instability in their apis.
I wonder if the chatgpt shirtless guy is from the same kind of issue
Other providers might have better isolated KV stores?
I've had this happen with ChatGPT too.
Is anyone even offering single tenant inference as a service?
Interesting. That might explain the few times I've received completely unrelated responses from a certain provider's API.
Is that a new class of tenant isolation vulnerability?
Seems not? Just good old gpu allocations colliding
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I kind of forgot about the shared tenancy attack model. I think I was assuming/hoping the software was magically isolating my tokens, when in reality that's impossible for shared tenants. There's whole classes of attacks on things like shared cache, before we get to the LLM-specific features and potential bugs. Enterprises with highly sensitive data should really be on non-shared hosts.
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At no point do we claim that it is a bug with the model. We specifically outline how it was a mis-configuration on Cloudflare's part and a bug in SGLang. We name the model in the title because our customers need to know which of our endpoints was affected. In fact, we say "the GLM-5.3 Workers AI model", not "GLM-5.3".
Not buying that. You explicitly mention the model name in the title the 'GLM 5.3 workers AI model' suggests it is something specific to GLM 5.3 which it is not. You mention Sglang and are all surprised that it didn't throw an error. But the issue was yours all along.