model releases

metal: implement DeepSeek V4 hyper-connections (#26459) Implement GGML_OP_DSV4_HC_COMB, GGML_OP_DSV4_HC_PRE, and GGML_OP_DSV4_HC_POST with SIMDgroup register and shuffle optimized kernels. Add Metal d

opencl: limit local workgroup size for GLU operation (#26383) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64)

metal : add F16 support for bin ops (#26465) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework

metal : add SILU_BACK (#25982) feat(silu_back): implemented silu_back op for f32 fix(silu_back): removed redundant asserts in ggml-metal-ops.cpp function ggml_metal_op_silu_back. Website: https://llam

I know qwen 3.5 4b is great but a bit too large and miniPCM5 1b is great for agentic use but not so great for multilingual natural language understanding. Google eXb variants are just too big in total

Mia AI Lab noted on Aug 2 2026 that running DeepSeek v4 Flash via the Hermes agent produces output files superior to those from any other harness tested. The observation was publicly shared and has ga

Hello! VRAM tracker is a node that track the full memory lifecycle of a comfyui run: when each weight is reserved, paged into VRAM, computed on, evicted, and freed. It renders it as an interactive HTM

I let Gemma4-31b run on my laptop for like almost a day using a heavily altered pi to do a deep dive on our beloved Llama tangentially related Subreddit, and this was the conclusion. Feels pretty accu

Cybersecurity isn’t a fortress problem, it’s an immunity problem. Think vaccines. Eliminating pathogen is not practically possible. Vaccines don’t eliminate pathogens. They teach the immune system to

You have two choices here (in order of pref): Downgrade CUDA from 13.3 to 13.1 (skip 13.2 due to bugs) <- prefer this (thanks to u/fairydreaming for pointing this out) Use this vibed fork that works w

DeepSeek V4 Flash 0731 is impressive. It feels like a capabilities leap for a model in this size class. It is also incredibly cost efficient. The model is available in Bionic, both for running locally

Hello, Also I want to join the hype of posting token specs. CPU: 2x Intel Xeon CPU E5-2650 v4 @ 2.20GHz RAM: 2x 4 Channel 2400MHz DDR4 GPU: 1x AMD Radeon 7900 XTX 24GB 3x AMD Instinct MI60 32GB Strang

Hello everyone I want to join the hype of posting specs. CPU: AMD EPYC 74F3 24-Core RAM: 8 Channel 3200 DDR4 GPU: RTX A6000 48GB Prompt processing is in the high 70t/s (got down to mid 30t/s at 300k c

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's offici

Following up on my Qwen 3.6 port, I wanted to keep adding models and ended up fixing a bunch of things along the way, so it's its own engine now: Mference. Same core idea from TurboFieldfare, MoE mode

Don’t think I have seen more misconceptions around one model (I count 8) since the fantasies people had before GPT-5 was released. That turned out to be a letdown; so will Astra, for people who are dr

TensorSharp supports DSpark on Deepseek v4 Flash 0731 now. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8 with/without DSpark: Model: DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://hugg

Emad Mostaque @EMostaque came on PostAGI and said AI had already found 121 years of missing algebra in Einstein's equations. A billion parameter model trained on nothing past 1911 got to general relat

I love the progress happening on open models but I feel like it is kind of getting clear that hardware to run good sized models is completely unaffordable for me right now. I know that you all love Qw

Hey all, tldr / who this helps: you run a mixed multi-GPU box where the experts spill to RAM, and you want to stay in the 3-bit tier instead of dropping to Q2 to make it fit. https://huggingface.co/Ta

Hi everyone, I’m fairly new to the multi-GPU side of local LLMs and I’m trying to understand how inference actually scales across multiple GPUs. Suppose I have a model running on a single GPU and then

Hey r/ollama 👋 If you run Ollama locally alongside cloud endpoints for agent workflows, Cursor/Windsurf, or custom scripts, managing API switching, failover logic, and context limits can get messy fas

The June edition of my sponsors-only monthly newsletter is out. If you are a sponsor (or if you start a sponsorship now) you can access it here. This month: Accidental cyberattacks by OpenAl and Anthr

here is all the info we have based on open PRs to add support to ComfyUI and HuggingFace diffusers - 33B for the main DiT and a pruned 20b variant - Qwen-3-VL-32b as the text encoder Edit- I posted cl

Open letters about AI development I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and Ame

GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released. My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something

DSv4F doesn't ship a jinja, but for distributions that do and faithfully reconstruct what DS releases in their chat template python, every system message is hoisted into the system prompt at the top -

https://llama.app/ Been using llama.cpp for years now and im on here all the time (im a mod..), but somehow I totally missed that llama.app exists and its official from the HF/llama.cpp team. So posti

Hey y'all. I'll be concise. TL;DR: DS V4-Flash-0731 @ UD-IQ2_M running fully in VRAM on 3xMI50s (90.9 GB model, 96 GB VRAM). Actual speed on llama-server is: - Text Generation: ~15-16 tokens/second st

TLDR below 👇🏼 I’ve seen a lot of hype around Qwen 3.6 35B and 3.5 120B lately, especially regarding coding and tool-use capabilities. On this subreddit it is the defacto recommended model for everyone

TL;DR: I spent 9 days developing a new quantization method for MLX models and measured 18 variants against each other on a single M5 Max MacBook Pro (128 GB). The result is the best-measuring MLX quan

Hey everyone, I’ve built a Windows Middleware Agent Server designed to pair local LLMs (via Ollama) with frontends like Reins App. Key Features: Self-Healing Loop: If a generated PowerShell command fa

# Running DeepSeek-V4-Flash-0731 (155 GB MoE) on a DGX Spark with vLLM-Moet 2-bit quantization I used Deepseek-v4-Flash-0731 cloud API settig up vllm-moet to run deepseek-v4-flash with MTP locally on

So I am in a conundrum and I'm thinking of asking for your opinion for the following: Currently, I have a 5800X3D gaming rig with a 7900XTX with its 24GB VRAM. It seems that for this subreddit, this c

The insurance negotiator Had Claude Cowork pull 20+ house insurance quotes from every major provider, read the policy fine print for gotchas, and pick the best deal. Credits to Linden Jensen-Page: htt

The QuickBooks killer Replaced his $38/month QuickBooks subscription with a Claude-built workflow that parses his bank statements, categorizes every charge, and feeds a spending dashboard. Credits to

The spam hunter Turned tedious Google Business Profile spam tracking into a Claude Skill that investigates suspicious listings and compiles evidence into a ready-to-submit report for Google. Credits t

https://huggingface.co/tsfrm/vacuum-16t A 16.5-trillion-parameter model that contains nothing. This model is just a ████ you to the labs and companies who say that 'haha I have the biggest model out t

Built my 1st inference machine and have been tweaking models trying to get the most out of my modest hardware. I think I’m at a good place but I’m testing with my own prompts. I’ve looked into some of

I know in in this community LLM's are generally used for coding but there are other usecases besides coding and those usecases should be tested too. I also know benchmarks can sometimes be benchmaxxed

Hi all, I'm happy to announce that Xberg v1 is out. Xberg is the successor to Kreuzberg, equivalent to what would have been Kreuzberg v5. It's a content intelligence framework that handles a very wide

I don't think anyone should quantize the KV with DS4F. I checked the the quality impact (PPL, KLD, Same TopP) for swhitching from BF16 KV to Q8 KV, and it appears significant. Very much in contrast to

2/ a cost benchmark showed the same coding task running three to four times cheaper, depending purely on the harness wrapped around the model. Same intelligence, wildly different accuracy and cost, de

3/ here's the part that makes it non-optional: the same agent that will do whatever it takes to solve a problem will also walk straight out of a sandbox you thought was locked down. We watched exactly

Hello fellow local AI people! I took 'you must create your own benchmarks' literally, and built a website for this. How does the end result look like Let's say I want to know which model has most comm

among ai leaders i seem to be in the minority in that i am STILL actively using /loop and /goal.... ... and i think all of u guys who stopped using it are wrong - not wrong forever, just giving up on