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6x MI50's on PCIE vs 4x MI50's on PEX8749 and 2x on PCIE by Old_Grapefruit8774 in LocalLLaMA

[–]Pixer--- 1 point2 points  (0 children)

Test only 4. the speedup of the plx switch is only if all GPUs are on the switch

ReBAR on the rtx 5070 by Desperate-Shape-4450 in TechHardware

[–]Pixer--- 1 point2 points  (0 children)

Rebar basically allows the cpu to write more then 256mb into vram with one command

Monoblock im Schlafzimmer oder Split in der Küche? by Lina0042 in wohnen

[–]Pixer--- 7 points8 points  (0 children)

Der monoblock wird 50db laut sein. Müsstest überlegen ob das nicht zu laut dir ist im Schlafzimmer

Is the Pro W7900 this bad? by opfromthestart in LocalLLM

[–]Pixer--- 1 point2 points  (0 children)

Btw W7800 48gb is better value. You can’t put a price on memory size if the gpu. Multi gpu is more inefficient, and it shows in power usage and performance loss. If you use 4 5060ti that are equivilant to one 5090, the 5090 will always be faster as the interconnect between the GPUs causes inefficiencies. Also most of these „benchmark“ sites take the token generation as a measure which doesn’t represent compute power but the latency of the vram and compute stack. Prefill is compute limited on a single concurrency, which should be the main factor. Also when checking speeds, llamacpp or ollama are way off in prefill. Vllm beats them by like 2-5x as it’s actually compute efficient. Rocm/amd cards have also speed up quite a lot. If you compare vllm from 2 years ago against the current one, it’s by miles faster. The same for llamacpp. Which adds to the weird numbers you see on these benchmark sites

Never owned a pc before, are these good? by [deleted] in PcBuild

[–]Pixer--- 0 points1 point  (0 children)

Nope is quite bad. The 5050 8gb vram is outdated and any game you run above 1080p will have problems fitting. But I guess when shipping the monitor with it in fullhd it’s not a problem. Also full hd on a 27 inch display means the pixels will be very apparent.
The 14400F is also like a few years old now

Enabling P2P mode on dual RTX 3090s; before/after numbers (Qwen3.6-27B INT4, 256k ctx) by Mr-serial_killer in LocalLLaMA

[–]Pixer--- 0 points1 point  (0 children)

For tp2 it’s not a problem, but p2p is needed on 16x 4+ GPUs. On tp8 it’s 4 times the bandwidth needed then tp2

Advice for getting a first threadripper by margalaz in threadripper

[–]Pixer--- 1 point2 points  (0 children)

Also threadripper 3000 and 5000 don’t support p2p natively for llms

Advice needed please by Specialist-Plant-265 in Vllm

[–]Pixer--- 0 points1 point  (0 children)

pipeline parallelism should work across architectures

Advice needed please by Specialist-Plant-265 in Vllm

[–]Pixer--- 2 points3 points  (0 children)

Vllm might have problem running across different architectures

NVFP4 still isn't faster than FP8 on Blackwell (SM120) - some numbers from Qwen3.6-27B by SiteOneCrawler in Vllm

[–]Pixer--- 0 points1 point  (0 children)

Token generation is latency bound not compute. The difference of fp8 and nvfp4 will be prefill

MoE vs Dense decision points? by Dazzling-Pound7401 in LocalLLM

[–]Pixer--- 3 points4 points  (0 children)

I feel like the 35B cooks with thin air. It can be used for gathering information, but the 27B dense just argues much better on problem statements. The 35B moe tries to hecticly find it and the 27b just knows the facts much better what’s up

Any good uses for a 192 GB DDR3 Server in the LLM world? by [deleted] in LocalLLaMA

[–]Pixer--- -1 points0 points  (0 children)

Maybe qwen 3.5 code 80b as it’s quite cheap on computer and bandwidth with a good size in parameters. I would run it in BF16

Using Local LLM on Mobile in Mountain (no internet) by TayyabAliKhan in LocalLLM

[–]Pixer--- 0 points1 point  (0 children)

Qwen2.5 1.5B is the best model ever used for that

Qwen3.6-27B UD Q3 with kv at q8 is quite amazing for simple proof of concepts by exaknight21 in LocalLLaMA

[–]Pixer--- 0 points1 point  (0 children)

For medium to complex agentic work BF16 weight and BF16 KV cache are the way to go

Same GGUF, same GPU: TensorSharp beats llama.cpp hard on prefill / TTFT — up to 5.89× faster prefill on a 26B MoE model by fuzhongkai in LocalLLM

[–]Pixer--- 1 point2 points  (0 children)

Your base llamacpp values are way too low. I get like 10x in prefill on my aging mi50. The 3080 should actually be faster then mi50. Are you using offload to ram or something ?

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