u/GiovaSan

Original post is below.

Additional context I did not share earlier:

• The hub is Dirigera, hooked via Ethernet to a Deco X50 mesh (3 routers)
• IPv6 is enabled, with SLAAC + DHCPv6
• Earlier did experiments disabling Fast Roaming and SIP ALG. No IGMP option is shared in the app

Not sure why, but I followed one of the comments below:

1. Disconnected the hub from power for a while, like 30 minutes
2. Reconnected the hub
3. Disabled Bluetooth on my phone
4. Reset the Bilresa light switch
5. Start the pairing process

And it just worked.

No freaking clue why. My guess is that disconnecting the hub for a long time forced a IPv6 configuration refresh.

Thanks to that, I can also access the hub via Thread Tools.

An additional thing is that I run AdGuard on my phone, so I explicitly whitelisted Google Play Services and Ikea Home Smart; disabling it earlier did nothing, having them whitelisted worked. Not sure if this was helpful, but it is worth mentioning.

That's all, now the original post:

---

As per title.

I am no Matter nor Thread expert, but working in IT I am used to experiment.

We've purchased recently a Matter light switch and struggled to connect it to the hub. Old Zigbee devices work with no issues.

With some digging, I found the Thread Tools app, where I can easily replicate the error: the Dirigera Thread Border Router rejects the connection with no explanation.

I've tried to:

1. Add the light switch via the official Ikea Home Smart app
2. Add the Thread network via the integration option in Google Home
3. Add the light switch via Google Home
4. Connect via the Thread Tools app

All of them fail. Case 2 allow the addition but all the devices result offline; in all other cases, the connection is directly rejected.

The only operation that I can perform is to reset the network. Other than that, it show no other signs of life.

I would love further input; the customer support just said they acknowledge the issue and will solve it "soon".

Hello everyone,
I'm on a laptop with AMD Ryzen 4800H and NVIDIA 2060 6GB RAM. Also, 32GB system RAM.

I've a decently working llama.cpp setup with Q8_0 KV quantization, MTP draft n 2 with quantization, context 64k. Overall, it has 100 t/s parsing and 25-30ish when outputting. It works well; uses all resources, but works fairly well.

I've searched for further optimization and found APEX quants and ik-llama.

The first should be a "pull new model, run" scenario.

The second is a bit more tricky. Flags are different from upstream and require llama-swap for routing, when official llama.cpp had built-in router.

I want thus to know if anyone has experience with this and whether performance does improve.

Verbose stuff:
My llama.cpp flags:

llama serve \
    --n-gpu-layers all \
    --jinja \
    --port 54321 \
    --verbosity 4 \
    --load-mode mmap \
    --flash-attn on \
    --kv-offload \
    --kv-unified \
    --reasoning auto \
    --reasoning-preserve \
    --parallel 2 \
    --threads 8 \
    --batch-size 1024 \
    --ubatch-size 512 \
    --cpu-moe \
    --cache-type-k q8_0 \
    --cache-type-v q8_0 \
    --spec-type draft-mtp \
    --spec-draft-n-max 2 \
    --spec-draft-type-k q8_0 \
    --spec-draft-type-v q8_0 \
    --ctx-size 65536

My ik-llama ones:

llama-server --port 54321 \
    --fit --fit-margin 512 --no-mmap \
    --defer-experts --flash-attn on --merge-up-gate-experts \
    --threads 6 --threads-batch 8 \
    --batch-size 2048 --ubatch-size 512 \
    --ctx-size 65536 --parallel 1 \
    --cache-type-k q8_0 --cache-type-v q8_0 \
    --k-cache-hadamard --v-cache-hadamard \
    --reasoning auto --jinja

Had issues setting up MTP with ik-llama, problem for later.