Espen JD @Snixtp
Cyber Network Engineer | Codex enthusiast | Local AI | RTX Pro 6000 enjoyer github.com/net-snix Joined June 2020-
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Nemotron 3 Ultra is on Huggingface 👀 550B total 55B active 350GB NVFP4 huggingface.co/nvidia/NVIDIA-…
Q6 MTP is also up for those who want a good balance between size and quality huggingface.co/notSnix/Step-3…
I've uploaded Q4, Q8 and BF16 MTP draft models in their own repo It should work natively with yesterday's llama.cpp merge huggingface.co/notSnix/Step-3…
@RedBedDread @DJLougen likely because better precision is the safer quality/acceptance default
I've uploaded Q4, Q8 and BF16 MTP draft models in their own repo It should work natively with yesterday's llama.cpp merge huggingface.co/notSnix/Step-3…
New Gemma 4 model 12B, which means it can run on 16GB cards
Meet Gemma 4 12B! A unified, encoder-free multimodal model designed to bring high-performance intelligence directly to your laptop, and released under an Apache 2.0 license. Bridging the gap between edge efficiency and advanced reasoning. Here is what’s new with Gemma 4 12B: 👇
Mmyes, but misleading. You’re not using PCIe as VRAM replacement every token. With model splitting, each 3090 mostly reads its own local VRAM, and PCIe is mostly moving activations/tensors between GPUs, its not streaming the whole model every token. That’s why you don’t see much of a performance difference between x8 vs x16, even tho it’s half the bandwidth.
@Yeghro Threadripper 7960x and Gigabyte TRX50 Aero D
Full model Q4_K_M Step 3.7 Flash: huggingface.co/notSnix/Step-3…
@TheAhmadOsman Please elaborate on fp8 kv cache on ampere
@bsilone I’ve gotten DeepSeek V4 Flash to run with 128K context. ~165GB model alone. Struggling with speed tho because of ds4 engine pop
I've uploaded my Q4_K_M Step 3.7 Flash GGUF MTP quant on Huggingface Let me know how it is please huggingface.co/notSnix/Step-3…
@faultbugs 3x 3090 Pro 6000 4080s I’m trying to get q4 DeepSeek v4 flash to run
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