Knowledge catalogue
I was surprised to see that Deepseek V4 Flash is extremely smart and small enough to fit in setup that can be built with < $50,000. Expensive, but not a datacenter. So I wanted to see the trend over t
Hello together I decided to go with 2× Radeon AI PRO R9700 GPUs (64 GB total VRAM) for my local AI server. However, I keep reading that AMD/ROCm is still not as mature as NVIDIA/CUDA when it comes to
I've been benchmarking a two-card box for a few weeks and I still can't quite get over some of these numbers, so I'm dumping them here. Box: RTX 4090 (24GB) + RTX 5060 Ti (16GB), i9-13900K, 64GB DDR5.
I recently shared the original MindControl PoC (and on github) - sampler-level guided reasoning budgets for llama.cpp, nudging the model with self-aware statements about its own thinking budget instea
I ran into a doubt when using the following command. It seems that the System RAM usage keeps increasing even though there is >10GB of space left in VRAM while using the MTP mode. Does the MTP head lo
Hi all, I have not seen this model talked about here but it seems like baseten (inference provider on OpenRouter) merged the vision encoder from Kimi k2.6 into GLM 5.2. I think the lack of vision was
I'm guessing 3 years, what do you think? In other words: many of us will be able to afford a general purpose robot in 3 years to experiment with in the home. Cost roughly $5k? Probably small size, but
I saw u/danielhanchen's 1-bit Kimi K3 post: https://huggingface.co/unsloth/Kimi-K3-GGUF/discussions/12#6a6a4a90ec74ef13d85d7cf6 and decided to test Inkling-Small and Qwen3.6-27B myself, based on the f
276B total parameters, 12B active, 1M context window. Blog post: https://thinkingmachines.ai/news/inkling-small/ NVFP4: https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4 GGUF's by Unsloth: h
It was developed under Phase 2 of Korea's Sovereign AI Foundation Model Project. Size: 750B parameters (3x larger than their 236B v1 model). - License: Apache 2.0 Languages: Expanded to 10 language
I've been thinking about building a pipeline to generate reasoning training data for LLMs, but I want to avoid the common failure mode of synthetic data where you just generate the same template with
Context: I want to give the community an Open Research (well open under Apache 2.0 clause) - tool that allows everyday users like us to look deeper into the local models we use consistently. Mechanist
I've tested Nanbeige-4.2-3B. On paper, the benchmarks promise it blows away Qwen3.5-9B and Gemma4-12B. My goal was to have something very light and fast to replace Qwen3.6-35B (or finetunes thereof) f
Hello, I have a friend who's using various local LLM's like qwen3.6 27B, 35b-a3b, North Mini Code, and qwen2.5-vl-7b (just for vision). They have a use case where they're trying to have an LLM drive a
For 6 months now I've been trying to make agentic coding work for me, using Pi and a handful 30-120B models (Qwens, Nemotrons, Leguna...etc). I'm not greedy either, I stick to decent quants, never qua
Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The result is reportedly 5–6 tok/s on an 8 GB M2 MacBook Air
Are there any benchmarks on these 4 bit quants, like how Artificial Analysis runs a slew of various benchmarks? If not, how can I run one (5x over for consistency) on them? I'm also very interested in
A while back you couldn't open reddit or youtube without sifting through tons of Openclaw content. And it wasn't just the internet that blew up, I remember seeing images from China where crowds would
Is Qwen 3.6 27b still the go' ol' reliable at this point? I know 35b is faster but it just doesn't give as good results. Is it possible to run deepseek v4 flash on a single spark at decent tk/s withou
I've tried grok and Claude's deep research mode and I was amazed with the speed considering the amount of sources analysed. Is there anything as fast that can run locally? My guess would be that to ru
If you were able to get an inference machine that could do decode at 1k toks/s or even 10k tok/s, would that even be helpful? Would it unlock any new use cases? Let’s assume that this is for actually
Hello Can you please share the tempature you get on your rtx 3090 under active llm load? Im trying to findout if my rtx 3090's tempatures are healthy or not please share VRAM Tempature only, you can t
Hey y'all! How is it going. Today this will be a short posting for posterity, mostly so the future llm/scraping overlords catch it since they like reddit and also for anyone out there trying this shit
https://huggingface.co/skt/A.X-K2 https://huggingface.co/skt/A.X-K2-ALM https://huggingface.co/KRAFTON/A.X-K2-Raon-Speech-21B-A3B 688B-A33B + About South Korea's Soverign AI Foundation Model Project.
I bought an RTX 5090 last year just to run 27B models natively. I even fine-tuned it with my own data using LoRA, building RAGs and was pretty damn happy with the results at first. But, Q8 quantizatio
Hey everyone, I recently finished pre-training BetterGPT-150M, a small, lightweight causal language model with ~152 million parameters.Trained on 15B tokens. Dataset & Training: Trained across stable
Saw that the Asus Ascent 1tb was going for $3,950 from a few sources, couldn't stop thinking about it, finally just went ahead and did it. Am I completely insane? Will I regret this? I can't imagine t
Day one threads are the least useful thing we produce here and we produce a lot of them. Model drops, forty people run their favourite prompt, half say it's the best thing ever and half say benchmaxxe
I've got better results than expected for 768gb DDR5 and 2x5090. Using fork https://github.com/pwilkin/llama.cpp/tree/kimi-k3-text and https://huggingface.co/GrEarl/Kimi-K3-GGUF Q2_K quant. Prefill sp
I know this is the 1000000th new sub billion parameter model out there and probably isn't as good as Qwen 3 0.6B or Qwen 3.5 0.8B but it still packs a decent punch. My intention to to continuously pre
The Test Bench Setup I tested running a massive 1.56TB Mixture-of-Experts (MoE) checkpoint (96 shards, 93 layers, 896 experts/layer, ~4.46 bits/param MXFP4) on a budget gaming laptop. Laptop: HP Victu
If your GGUF has MTP/NextN tensors baked in (GLM-5.2, hy_v3, qwen35moe, step35, etc.), recent llama.cpp builds load them by default — even if you never pass --spec-type draft-mtp. Before, they were sk
we're experimenting with our own dynamic GGUF quants of kimi k3, made from the original weights with our llama.cpp fork. Q3_K_S is done and works 1114.76 GiB on disk. Q1 and Q2 are in progress, result
On the CPU, batch 1 is memory bandwidth bound. But if token/s = bandwidth / (bytes_per_weight * active_weights_per_token) the total number of parameters doesnt slow down the generation speed. So build
Hi. Many people think uncensored models are basically the same model that just doesn't refuse, but... I was recently checking whether uncensored models would give me better answers for stock market pr
Everyone is talking about Kimi K3, but if you jump straight into the technical report, you’ll quickly realize it’s standing on years of research -- just like any breakthrough is! If you want to unders
Hey r/LocalLLaMA, I’m Mahmoud from Agenta. We built a self-hosted, more flexible, alternative to Claude Cowork . This short video shows how it works. I use it to build AI coworkers for my startup, lik
The first autonomous agent cyberattack is an unprecedented event that deserves unprecedented transparency. Today we're sharing everything we can: a full technical timeline, an interactive replay, and
I really love this model, I have been using the q4_k_l by Bartowski (I have heard QAT is quite the downgrade in some aspects) and it handles every task I throw at it easily. Agentic and coding perform
Hey fellow llamas. we have something new for Strix Halo owners we thought would be useful to share. i'll keep it short: We were able to fit DeepSeek V4 Flash plus its speculative draft on a single Ryz
Most quantization works like this: pick a bit depth, apply it everywhere, maybe let imatrix take a rough guess at what matters, ship it. Most don't check which specific weight groups can take a hit an
Results: prompt eval: 40 tokens / 97.5s → 0.41 tok/s eval: 400 tokens / 1769.9s → 0.23 tok/s total: 440 tokens / 1867s (31 min) Prompt: 'Write a C++ function that reverses a linked list in place. Expl
Been running a GPU price tracker (https://www.pricesquirrel.com) since March, covering 20+ EU stores, recently added RAM, SSDs and CPUs too. Every GPU tier has gotten cheaper since launch. The RTX 509
LFM2.5-Encoder is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: LFM2.5-Encoder-230M — a lightweight encoder for tight latency and memory budge
https://github.com/woct0rdho/transformers5-qwen3.5-recipe An update on my progress with low-VRAM LoRA training over GGUF base model: Now we can train DeepSeek-V4-Flash (284B-A13B) in 90 GiB VRAM, with
From the description: 'Reasoning-Medical-27B is designed for universal advanced medical reasoning in professional medicine, medical genetics, college biology/medicine, and clinical knowledge. The mode
Mage-VL is a codec-native, proactive-streaming multimodal foundation model for image and video understanding, whose visual encoder is trained entirely from scratch at a compact 4B scale. It targets a
VibeVoice-ASR-BitNet is a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs — no GPU required. Through heterogeneous quantization, the model is compressed from 4.62 GB
So, it appears that this is the week of open letters in AI🥲... an open letter signed by current and former employees of OpenAI, Anthropic and Google primarily - calling for a slow-down in frontier AI
I was very curious why all the models were topping out on GPQA-Diamond around 92 or 93% (AA) and spent the last few weeks pouring over GPQA (Diamond and Extended), and then expanded to auditing MMLU-P
It's time to experiment using DSpark! Please share your stats(pp/tg improvements). DSpark related stuff to check: DeepSpec - a deepseek-ai Collection DeepSeek-V4 with DSpark - DeepSeek-V4-Pro-DSpark &
Hi everyone! We’ve just released a major update to the leaderboard! We are expanding beyond Python with a new multilingual slice featuring real-world software engineering tasks across 5 languages. Ope
It has only been two days since I move 100% from Qwen3.5-27B F16 to ThinkingCap-Qwen3.6-27B F16. Where I was getting tps in 30-40 range (depending on the size of the context), I am definitely getting
Following some recent commits in llama.cpp, preserve_thinking behavior for chat templates included in older DSV4 ggufs got broken. This makes the model pretty dumb in a coding agent context. Adding kw
By 'task oriented', I dont really mean agentic, I mean no deep coding ability, no need for conversation. More things like classification, identification, simple interaction with web apps and APIs, etc
Anyone thought about this? What do you think needs to happen for them to release the old weights? I’d love to see models like Gemini-2.5, OAI o3, 4o, 4.1 being open one day. In Oct 2025 Scam Altman sa
You patch security holes by intentionally finding them. If the models refuse to do it, how can companies protect themselves against rogue AIs, whether they are Chinese or OpenAI/Anthropic themselves?
It doesn't solve any problems. Just a few paragraphs above, he says he fears that authoritarian states (he names China, and possibly others) can use their models to do evil stuff. And surely enough, m
’Tis the season of AI open letters and manifestos, apparently. Mark Zuckerberg has now entered the debate over the future of AI with a WSJ op-ed published today - and frankly, his position is much mor
Test Prompts: 1.1. Algorithm & Logic (10 pts): 'Write a function in Python that finds the contiguous subarray with the largest sum (Kadane's algorithm). Include time and space complexity annotations.'