CompanyAnthropic8 recent entries23 Jul 2026DeepSeek Founder’s 4-hour investor meeting: DeepSeek is prioritizing AGI over user growth and commercialisationA Chinese article compiled 52 remarks from Liang Wenfeng’s four-hour investor meeting. I’ve summarised the most important ones below. DeepSeek has one central objective: AGI. This is not the time to m→4 Aug 2026Why are Chinese models better* at Frontend than the western top labs?I use A LOT both openAI and Anthropic products. When I need some frontend work (pure web dev) (or answer that feel less verbose and more to the point) I use Anthropic. For multimodality openAI feels b
CompanyOpenAI8 recent entries4 Aug 2026Why are Chinese models better* at Frontend than the western top labs?I use A LOT both openAI and Anthropic products. When I need some frontend work (pure web dev) (or answer that feel less verbose and more to the point) I use Anthropic. For multimodality openAI feels b→5 Aug 2026MoE CPU-offload benchmark on Deepseek V4/Gemma4/Qwen/GPT-OSS — TensorSharp vs llama.cppTensorSharp's MoE CPU-offload feature has been merged into main. Here is the parameters description of this feature: Mixture-of-Experts CPU offload: --n-cpu-moe <N> | -ncmoe <N> Keep the routed MoE ex→6 Aug 2026I ported vLLM's serving stack to C++20: 66 MiB binary, no Python at inference, output checked token-for-token against vLLMI'm the author, so discount the enthusiasm accordingly. This is an unaffiliated community port, not endorsed by the vLLM project, which it uses to verify its correctness. What started it: I love vLLM,→9 Aug 2026It took two years, but we finally have a 'local Sora'Who remembers when OpenAI previewed Sora two years ago and the quality felt unreal? We had never seen anything like it. Back then, Sora 1 didn't even generate audio and was heavily censored. Prompt: i→14 Aug 2026Fixed/improved Jinja chat template for Qwen 3.8'Again?' you might ask. I(*) took the original Qwen 27B 3.8 chat template and compared it to the improved template that was posted a day before. The original has issues. The improved one fixed some wh→23 Sept 2026I turned Qwen3.8-27B Q2_64 + llama.cpp into a fully TypeSafe AI-compatible Jev-like system. OpenAI API still intact! World’s first Vision-enabled Jev-like model! <10 GB VRAM, 170 ms on an RTX 3090 and ~140 tok/s in chat. 76% vs. 88% Jev-1.13 Acc. on a diverse 22,000-request typed-decision benchmarkYesterday I released Bonsai-Llama-Jev - a Jev-like Typed Decision inference system for locally operated, sovereign AI. In my new typed-decision-bench, covering more than 22,000 decisions across 175 us→12 Oct 2026A 'free' multi-modal 'embedding' model (already) included with your LLM: vLLM plugin + upstream PRDisclaimer: I work for NuMind, the company behind this plugin. TLDR: Text/image (simili-)embeddings from the LLM you already have loaded :-) I've been working on making it easier to obtain embeddings →13 Oct 2026Running decision models: Picking quant, VRAM usageHi, I see that vllm released d3 (a family of decision models) and llama.cpp already has support for them. I want to try them out. Are decision models sensitive to quantization? I know that for autocom
CompanyGoogle8 recent entries16 Apr 2026Google’s DeepMind just released new 4B and 27B MedGemma models!Google DeepMind released MedGemma, a collection of medical vision-language foundation models based on Gemma 3 in 4B and 27B parameter sizes, demonstrating advanced medical understanding and reasoning →4 Jun 2026Best Visual Reasoning Model in 2026 (Including APIs) [D]Gemini 3.1 Pro and Gemini 3-Pro lead visual reasoning benchmarks , with GPT-5.2, Kimi-K2.5, and GPT-5.2-Pro following . A 2026 evaluation benchmarked 15 leading multimodal models on visual reasoning a→30 Aug 2026We used HFlow to evaluate the latest open weights VLMs for processing egocentric dataWe used HFlow to evaluate the latest open weights VLMs for processing egocentric data. This was based on Build AI's Egocentric-10k evaluation, which used Gemini 2.5 Flash to measure hand visibility an→23 Sept 2026Which small model works best with Ollama on a Raspberry Pi 5?I've been comparing and working on Gemma 4, Phi-4-Mini, and Qwen3-4B for local LLM use on an 8 GB Raspberry Pi 5. The results were indeed close, but each model has a different strength: Gemma 4 E2B: a→6 Oct 2026Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Googlehttps://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combina→6 Oct 2026google/embeddinggemma-2 · Hugging FaceEmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimens→10 Oct 2026Open-source Mac app that runs EmbeddingGemma 2 locally to search your files by what’s in themDigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and l→12 Oct 2026Explore DNA with DeepMind’s AlphaGenome and in-browser MedGemma 1.5 4B via WebGPUHey everyone, Sharing an educational tool called AskCyph Health Explorer to explore how single-letter DNA mutations lead to disease, how they alter 3D anatomy, inheritance probabilities, and available
CompanyMeta8 recent entries16 Aug 2026Based on an accelerating frontier -> local trajectory, expect a ~30b param 'Mythos at home' by as soon as Jan 2027 (rationalisation below)Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below: How I chose the comparisons The basic question I’m trying →20 Aug 2026Ornith-1.5-35B-A3B Q4 running 60tk/s on 4070Ti.Not everyone has the disposable income to build a small data center, so making this post for the underdogs as I was very surprised by the performance/results of this 35B MOE model. Context is admitted→25 Aug 2026tencent/WeMM-Embedding 9B/4B/2BWeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-n→2 Sept 2026Which one will you choose and why, between R9700 32GB vs W7800 48GB?I'm planning to upgrade my workstation (linux with 5700X/64GB DDR4) for local inference and pytorch training. I'm trying to decide between: 2× AMD Radeon AI PRO R9700 32GB 2× AMD Radeon PRO W7800 48GB→6 Sept 202648 tg/s 440 prefill on my grandma's cluster (2xP40) (sort of)TL;DR: switching KV cache to f16 may give a boost in speed if using MTP and ngrams. I have a self-built 'AI mega-cluster' with 2x P40s on a cheap Chinese motherboard and a Xeon CPU (around $1,100 to b→16 Sept 2026I built a native Vulkan training backend for 143 modern Transformer architectures — no CUDA or PyTorch requiredI've been working on something that started as the training backend for my Hierarchos architecture, but it has grown into a much broader project: https://github.com/necat101/Hierarchos-Native Hierarch→23 Sept 2026I turned Qwen3.8-27B Q2_64 + llama.cpp into a fully TypeSafe AI-compatible Jev-like system. OpenAI API still intact! World’s first Vision-enabled Jev-like model! <10 GB VRAM, 170 ms on an RTX 3090 and ~140 tok/s in chat. 76% vs. 88% Jev-1.13 Acc. on a diverse 22,000-request typed-decision benchmarkYesterday I released Bonsai-Llama-Jev - a Jev-like Typed Decision inference system for locally operated, sovereign AI. In my new typed-decision-bench, covering more than 22,000 decisions across 175 us→7 Oct 2026Why is ik_llama.cpp said to be faster than Mainline? On my hybrid multi-GPU rig, Mainline easily beats itI constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainlin
CompanyMistral3 recent entries23 Jul 2026DeepSeek Founder’s 4-hour investor meeting: DeepSeek is prioritizing AGI over user growth and commercialisationA Chinese article compiled 52 remarks from Liang Wenfeng’s four-hour investor meeting. I’ve summarised the most important ones below. DeepSeek has one central objective: AGI. This is not the time to m→6 Aug 2026nvidia/NVIDIA-Nemotron-Parse-2.0 · Hugging FaceNVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-readable representations with text, layout classes, bounding boxes, and reading-order information. Given a Red, Green, Blu→30 Sept 2026Follow-up: my native Rust + Vulkan Transformer training backend — 14 days later, now 14 parity-verified architectures and full PEFTFollow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a
CompanyDeepSeek8 recent entries28 Aug 2026Tests de maîtrise du français : les modèles chinois (DeepSeek, GLM, Kimi) face à une grille critériéePost rédigé avec l'aide de GLM5.3 flash Contexte : formateur professionnel d'adultes en France, j'utilise des agents LLM au quotidien et le français qu'ils produisent est mon critère n°1, avant les be→1 Sept 2026All currently popular local models in one table + Opus 4.8 resultsIf you are thinking what model will fit best your HW specs and tasks you are doing here is one table with all currently popular models that still can be considered as local. LLM Test Scores Feature De→8 Sept 2026DeepSeek Flash 4.1 is already being tested via API and rolling out.Translation: 'Internal beta testing for an intermediate version of DeepSeek V4.1 Flash is now open; you are welcome to try it out. It adopts a new model architecture featuring native multimodal suppor→10 Sept 2026DeepSeek V4.1 Flash: Stronger, Faster, More AccessibleOriginal Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our→10 Sept 2026DeepSeek V4-1 Flash is outHere we go again, DeepSeek is back again with a new model V4-1 Flash A multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens Mark→16 Sept 2026I built a native Vulkan training backend for 143 modern Transformer architectures — no CUDA or PyTorch requiredI've been working on something that started as the training backend for my Hierarchos architecture, but it has grown into a much broader project: https://github.com/necat101/Hierarchos-Native Hierarch→30 Sept 2026Follow-up: my native Rust + Vulkan Transformer training backend — 14 days later, now 14 parity-verified architectures and full PEFTFollow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a→14 Oct 2026A 35B multimodal model running on a phone with ~3GB memory? MiniCPM-V 4.7 demo is pretty wildJust saw this demo from OpenBMB for MiniCPM-V 4.7, and the on-device capability is pretty impressive. A 35B-A3B MoE vision-language model running locally on mobile devices is already interesting, but
CompanyNVIDIA8 recent entries16 Aug 2026I'm going crazy. Why does it use the CPU...I don't know what's happening. So, when i use models in openwebui everything works, ollama uses the GPU fully, everything's perfect. Then, I use Hermes, and loads the model in VRAM (fully, no offload →20 Aug 2026Ornith-1.5-35B-A3B Q4 running 60tk/s on 4070Ti.Not everyone has the disposable income to build a small data center, so making this post for the underdogs as I was very surprised by the performance/results of this 35B MOE model. Context is admitted→2 Sept 2026Which one will you choose and why, between R9700 32GB vs W7800 48GB?I'm planning to upgrade my workstation (linux with 5700X/64GB DDR4) for local inference and pytorch training. I'm trying to decide between: 2× AMD Radeon AI PRO R9700 32GB 2× AMD Radeon PRO W7800 48GB→6 Sept 202648 tg/s 440 prefill on my grandma's cluster (2xP40) (sort of)TL;DR: switching KV cache to f16 may give a boost in speed if using MTP and ngrams. I have a self-built 'AI mega-cluster' with 2x P40s on a cheap Chinese motherboard and a Xeon CPU (around $1,100 to b→16 Sept 2026I built a native Vulkan training backend for 143 modern Transformer architectures — no CUDA or PyTorch requiredI've been working on something that started as the training backend for my Hierarchos architecture, but it has grown into a much broader project: https://github.com/necat101/Hierarchos-Native Hierarch→30 Sept 2026Follow-up: my native Rust + Vulkan Transformer training backend — 14 days later, now 14 parity-verified architectures and full PEFTFollow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a→7 Oct 2026Why is ik_llama.cpp said to be faster than Mainline? On my hybrid multi-GPU rig, Mainline easily beats itI constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainlin→7 Oct 2026d1-3B and d1-omni from LiquidAIhttps://preview.redd.it/owhrvbbiu2uh1.png?width=4096&format=png&auto=webp&s=55d816753e7adee0e1391c9f581052b4bde44da7 d1-omni-600M d1-omni-600M is a 600M parameter decision model built on LFM2.5-Encode