I've had a variety of relationships.

Every time I breakup, I enter this massive depression. I find myself re-playing conversations and arguments and other such things over and over trying to fill in information I can't know and understand about my partners.

Lots of the times, I finding that I'm by myself talking to myself, or walking somewhere and feeling some sort of anguish or angst that causes me to twitch. It's been so hard to catch. I still have dreams about them. It makes waking up hard.

This last one has fucked me up quite a lot. Poly. Seemed like they were monkey branching to me then boomeranging. I keep trying to understand. To find a narrative. I keep trying to wonder if it was me or if it was them or what I did wrong or what they did wrong. There were a lot of things we associated with each other, so a lot of things trigger to remind me of them throughout the day. I've broken my OCD and tried to message them through means even though she blocked me. I just keep finding other means. I messaged her in anticipation of my problems so she can block me in certain places. Sometimes I fail. I've failed recently.

I feel like it's a bunch of things. A bunch of things I need to do ERP with. If someone else has gone through this, I'd appreciate knowing how you got out. It's been 6 months since we broke up, we dated for 3.

I have a 5070 and a 5060ti in my machine. I know in Windows both are usable even concurrently. I'm trying to figure out why only one is showing up.

Both PCIe slots are showing. Got this from the logs:
I have a 5070 and a 5060ti in my machine. I know in Windows both are usable even concurrently. I'm trying to figure out why only one is showing up. Both PCIe slots are showing. Got this from the logs:

[    5.752828] nvidia 0000:04:00.0: vgaarb: VGA decodes changed: olddecodes=io+mem,decodes=none:owns=io+mem
[    5.765243] nvidia 0000:2b:00.0: enabling device (0000 -> 0003)
[    5.765367] NVRM: This PCI I/O region assigned to your NVIDIA device is invalid:
[    5.765428] nvidia 0000:2b:00.0: probe with driver nvidia failed with error -1
[    5.765470] NVRM: The NVIDIA probe routine failed for 1 device(s).
[    5.765474] NVRM: loading NVIDIA UNIX Open Kernel Module for x86_64  580.159.03  Release Build  (dvs-builder@U22-I3-AM27-29-6)  Fri Apr 24 06:03:03 UTC 2026

It sucks being the first in your family to go to therapy.
It sucks accumulating label after label.
It sucks to be able to power through something and have no explanation, then being aware of said thing and hypersensitive to it.
It sucks to have empathy for your parents who didn't have the means that you did, but still suffer from the issues you're aware of.
It sucks that you can't convince them.
It sucks that they can't comfort you.
It sucks that they don't understand when you're upset.
It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks. It sucks.

I'm tired.

For those who want to run latest dense ~30b models and only have 16GB VRAM, if you have a old card with 6GB VRAM or more, plug it in.

It matters that everything fits on the VRAM, even on 2 cards. Even if one of them is quite weak.

I have a 5070Ti 16GB and a old 2060 6GB. The common idea is you need 2 same GPU to maximize performance. But one day I was strike by the idea, why not give it a try?

Let's see, if you did not bought a mother board just for LLM, it's very possible you have a true PCI-E x16 slot and a couple that looks like x16 but are actually wired with x4, just like me. That's a perfect slot for a old card.

16GB + 6GB = 22GB, it's getting close to the 24GB class card. If you have a better old card, lucky you!

Then you use llama-server with a config like this

[*]
jinja = true
cache-prompt = true
n-gpu-layers = 999
no-mmap = true
mlock = false
np = 1
t = 0

[qwen/qwen3.6-27b]
model = ./Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf
mmproj = ./Qwen3.6-27B-GGUF/mmproj-Qwen3.6-27B-BF16.gguf
reasoning = on
dev = Vulkan1,Vulkan2
c = 128000
no-mmproj-offload = true
cache-type-k = q8_0
cache-type-v = q8_0

A couple specific points:
- dev=Vulkan1,Vulkan2, this enables the two GPUs, run `llama-server.exe --list-devices` to see what you should set.
- no-mmap and mlock=false keeps the model away from your RAM
- np=1, no-mmproj-offload (or do not supply mmproj model), cache-type-k and cache-type-v to minimize VRAM needed
- n-gpu-layers=999 to prefer GPU offloading, well this may be unnecessary, but I'd keeps it
- split-mode=layer to split the layers asymmetrically across the device, "layer" is the default though so you don't see it above.
- c=128000 could be a little stretch, but works well enough for me.

BTW I also have intel integrated GPU that I plugged the monitors into, which is Vulkan0.

Some numbers, basically, at 128k max context, 71k actual context useage, pp=186t/s and tg=19t/s, quite usable speed compared to the 4t/s on single card.

[56288] prompt eval time =    5761.53 ms /  1076 tokens (    5.35 ms per token,   186.76 tokens per second)
[56288]        eval time =   58000.15 ms /  1114 tokens (   52.06 ms per token,    19.21 tokens per second)
[56288]       total time =   63761.69 ms /  2190 tokens
[56288] slot      release: id  0 | task 654 | stop processing: n_tokens = 71703, truncated = 0

Edit:

Some folks want numbers, so here is llama bench. This is with cuda instead. Runs with --device CUDA0 are on single GPU. Without uses all GPU. It's fairly clear fitting on GPU, even on a second weak one, matters a lot for tg speed, especially at long context.

llama-b8948-bin-win-cuda-12.4-x64/llama-bench.exe \
    --model ./lmstudio-community/Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf \
    --device CUDA0 --fit-target 64  -d 8192,16384

| model                          |       size |     params | backend    | ngl | dev          |       fitt |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------ | ---------: | --------------: | -------------------: |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |   pp512 @ d8192 |       903.13 ± 26.25 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |   tg128 @ d8192 |         16.54 ± 0.14 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |  pp512 @ d16384 |        663.60 ± 9.22 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |  tg128 @ d16384 |         12.03 ± 0.08 |


llama-b8948-bin-win-cuda-12.4-x64/llama-bench.exe \
    --model ./lmstudio-community/Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf \
    --fit-target 64 -d 8192,16384

| model                          |       size |     params | backend    | ngl |       fitt |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------: | --------------: | -------------------: |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |   pp512 @ d8192 |        769.00 ± 4.50 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |   tg128 @ d8192 |         25.40 ± 0.30 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |  pp512 @ d16384 |        668.83 ± 2.83 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |  tg128 @ d16384 |         24.31 ± 0.09 |


llama-b8948-bin-win-cuda-13.1-x64/llama-bench.exe \
    --model ./lmstudio-community/Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf \
    --device CUDA0 --fit-target 64 -d 8192,16384

| model                          |       size |     params | backend    | ngl | dev          |       fitt |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------ | ---------: | --------------: | -------------------: |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |   pp512 @ d8192 |       981.43 ± 27.91 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |   tg128 @ d8192 |         16.87 ± 0.17 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |  pp512 @ d16384 |       751.15 ± 16.03 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 | CUDA0        |         64 |  tg128 @ d16384 |         12.08 ± 0.12 |


llama-b8948-bin-win-cuda-13.1-x64/llama-bench.exe \
    --model ./lmstudio-community/Qwen3.6-27B-GGUF/Qwen3.6-27B-Q4_K_M.gguf \
    --fit-target 64 -d 8192,16384

| model                          |       size |     params | backend    | ngl |       fitt |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------: | --------------: | -------------------: |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |   pp512 @ d8192 |        807.61 ± 7.40 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |   tg128 @ d8192 |         24.85 ± 1.57 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |  pp512 @ d16384 |        732.96 ± 3.86 |
| qwen35 27B Q4_K - Medium       |  15.40 GiB |    26.90 B | CUDA       |  99 |         64 |  tg128 @ d16384 |         24.40 ± 0.07 |

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