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Show HN: Getting GLM 5.2 running on my slow computer

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Re: Show HN: Getting GLM 5.2 running on my slow computer

#231

Earlier quoted context omitted.

Now many mini-PCs and desktops are able to read simultaneously from 1 PCIe 5.0 SSD and 1 PCIe 4.0 SSD. This can ensure a reading throughput around 20 GB/s, i.e. 20 times faster than on author's system. With only 1 PCIe 5.0 SSD, the reading throughput is still significantly more than 10 times faster than on author's system. So it is likely that inference speeds around 1 token/s are achievable on something like a NUC m…

I'm curious, where are you seeing M.2 2230 to M.2 2280 size NVME SSE that exceed 4.5 to 5GB/s sequential reads for large files such as a GGUF (likefrom an ordinary ext4fs file system with default options)? The PCI-E 4.0 or 5.0 bus they're attached to might be capable of greater speeds, but the bottleneck is the flash and the flash controller. The benchmarks I'm seeing for many of them don't really make me think that…

I do not think that I have ever seen any benchmark for a PCIe 5.0 SSD that did not have sequential read speeds well over 10 GB/s.

If there were such a slow SSD, it would not make sense to buy it instead of a cheaper PCIe 4.0 SSD.

For PCIe 4.0 SSDs, I have seen a very large number of benchmarks where the SSDs achieved read speeds close to the theoretical limit, i.e. around 7 GB/s.

Searching now randomly for recent SSD reviews, I find many reviews for "SanDisk WD_BLACK SN8100", which achieve between 13 GB/s and 15 GB/s reading speed, which is better than most other consumer PCIe 5.0 SSDs.

Of course, if you write a very simple program that invokes something like "fread" or "read" in a loop, you will not reach such speeds. Achieving a SSD throughput close to the limit requires a more complex program that can ensure that the SSD controller is permanently busy with pipelined read commands.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#232
post #182
post #26

I was actually just working on the same thing as this, but I went down the route of mmapping the entire model into memory to avoid the extra ram usage. I also had Claude implement Medusa[1] on the model to try and avoid loading an additional model into memory but still get the benefits of MTP. Currently at a stop light so I can't list everything and I didn't get to read your full post either yet. To expand since I ju…

Not sure if mmapping is the right way. In my own tests I noticed that simple mmapping will produce many small reads and not keep the SSD queue saturated. So if RAM is large enough to cache most experts, that is a rounding error. But if the base weigths without experts fill more than half or RAM and you basically need to load in a few experts for each layer of each token, the latency gets important and mmap sadly bloc…

I agree that the maximum SSD performance can be achieved with carefully planned asynchronous reads, using either the modern liburing (io_uring) or the older libaio (Linux asynchronous I/O).

Nevertheless, I do not think that "this is not really possible with mmap, even with madvise".

If the kernel is not eager enough to prefetch pages from the SSD when you use madvise with MADV_WILLNEED, then you can use madvise with MADV_POPULATE_READ, which should force the reading of the pages that you request.

Using madvise with MADV_POPULATE_READ for appropriate page ranges at the right moments should be able to provide a performance not much lower than when using explicit asynchronous reads.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#233

Earlier quoted context omitted.

The funny thing is Claude Cowork has taught me to be patient with response timelines. I’m now figuring I’ll be running locally no later than 2028. (I want to spend no more than $10k. And I want to run a model comparable to today’s SOTA.)

> (I want to spend no more than $10k. And I want to run a model comparable to today’s SOTA.) The question is, will you want to run a model comparable to today's (meaning 2026) SOTA in 2028? Humans always want the latest shiny LLM model.

> I want to run a model comparable to today’s SOTA.

It says so in the quoted text, yes.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#234

Earlier quoted context omitted.

I have almost completely stopped writing real in my own output as a direct consequence of this. Maybe next year they'll catch up with my alternative wordings and I'll end up switching back

I’ve started learning into the various AI-isms in my prompts. Most recently, em dashes to chain related information in a single sentence. “Something something main point — but also pay attention to this related thing — finish main point.” Anecdotally, it seems to produce more consistent results when I “speak its language”. On the output side, I’ve become much more aggressive about trimming and editing any prose it pr…

So you’re code switching to incorporate AI-specific ‘vocal fry’ in order to better communicate with it?

That’s not just mimicry. It’s insightful. And it just might work. ;)

Re: Show HN: Getting GLM 5.2 running on my slow computer

#235
post #98

Earlier quoted context omitted.

For most projects the more practical solution is to use clouds offering GLM 5.2 for free. 1 token per minute is minuscule compared to their rate limits for free usage.

But it's about the journey not the destination. My current running local LLMs train of thought...

And it's also about privacy. I just can't wrap my head around the fact people completely ignore that aspect when they compare on-premise and cloud solutions.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#236
I only have 16GB of RAM in my laptop, but I would love to run a powerful model like that even at 0.5 tokens/s. For the questions I am asking, such a speed is more than enough. If anyone has any suggestions, please let me know! I am not an expert on LLMs, just a humble mathematician.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#238
post #207

After spending way too much time with Fable a few days ago, I noticed a new hallmark of AI generated text is using the word "honest" everywhere, in a somewhat self congratulating way. Some things that give it away to me: - "Honest numbers (WSL2, 12 cores, 25 GB RAM, NVMe via VHDX)" - "an honest peak projection (working set, KV, MTP row, reconstruction buffers) so the kernel OOM-killer never fires." - "Honest caveat f…

I've been seeing a lot of usage of the word `real` from recent fable sessions: - That gap is the real story: 3,873 flows - the real conversion filter types are: - I'll update the breakdown query to include a column for each real type

It aligns pretty well with corporate and pr-speak, but the LLMs really weaponize it and tire out the phrases quickly. I bet this is interesting for linguists.

Re: Show HN: Getting GLM 5.2 running on my slow computer

#239
post #107

Earlier quoted context omitted.

Could you expand more on what you do with qwen3.6? Because I couldn't get the denser 27B version to do trivial "take this pattern, repeat it over a single file with minimal thought, just slightly beyond what I can do with sed" reliably.

How was qwen3.6 launched? The thing is, everyone has their own variant of "qwen3.6 27b" depending on the launch parameters, ranging from "SOTA in its class" to "completely broken"

Yeah, I really should know. But I'm using whatever ollama gave me by default, which probably isn't optimal.
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