Search: “r-localllama”

Personally, I think the whole 'AI (LLMs) is going to take over' is just BS marketing. They've been pushing this doom-and-gloom since 2019, and we all know the models back then were nowhere near as cap

I reallise this is not exactly a locally hostable model, at least definitly not on my hardware. But I figured some of you might have some insight on this. I run an agentic coding benchmark. The model

I had all the data saved from AA's v4.1 index, so when they upgraded it in the wake of Astra's release, I could actually generate a before/after comparison. All intelligence and price per task are sam

I know I know, it's great, we know. I've been working on tweaking inference engines for a week now and it's been one shotting most of my vague prompts without any issues. It will even write tests and

So we’re seeing how Anthropic and OpenAI are gunning for regulations. I think most of us realize that this is a ploy for them to achieve regulatory capture, thus securing their moat and kicking out op

Edit: Spelled Baseten not Base-10 Full episode of this available at https://www.youtube.com/watch?v=PrSf7IOYu-I It's interesting to see how Dwarkesh has had to come around to the evidence that we are

Do you have something you'd like to share with the r/LocalLLaMA community. This is the place for it! Recommendation on presentation: Please share plain english description of what your project does an

Note - This is translated from the actual blog link right at the bottom. A few days ago, DeepSeek v4.1 was released. It raised the ability of small models to a new level. AI is improving much faster t

https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-3 AA shipped a new benchmark last week as part of the Intelligence Index v4.3 update — a brand-new private eval that re

From initial testing it seems pretty solid so far. Asked it to compile the latest llama.cpp for CUDA and its doing well so far. If this thing holds up to its score then its SHOCKINGLY good for its siz

I'm like you guys and am constantly experimenting with new models, seeing what they're all good at, how I can make use of them for certain projects and goals. I've been using Qwen 3.8 27b for minor co

Model: https://huggingface.co/internlm/Intern-S2-397B Collection: https://huggingface.co/collections/internlm/intern-s2 From Intern Large Models on 𝕏: https://x.com/intern_lm/status/209942518458737097

I'm currently using Qwen3.5 4B as the brain of my local AI assistant because my hardware is relatively limited. One thing I really like about it is the speed. On my system, I'm getting around 40–50 to

The full K2 Horizon lineup is out on Artificial Analysis. The AA intelligence vs. parameters plots show that - 0.9B and 375B are bad - 3.7B and 7B are SOTA - 36B A4B is SOTA for hardware with poor mem

Pushed out this new update, hopefully decreases the instances of crashes. I torture tested this one for ~12 hours after my fixes and found no instability. Q4 K XL needs more fine tuning, which I will

Hello Reddit. Posting this for fun. I thought it was a lonely and silly journey to set up Qwen 3.8 Next on a system that doesn't really run it properly—it was a challenge that might help the community

Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe o

Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe

I am considering dumping my ChatGPT Plus subscription and go full local, but to do so I would first need to reach a decent result for quality (and reasonable speed). My 4090 fried itself out of nowher

I finished my home inference server. First I tried Lenovo p620 workstation and while it’s a good value overall it pissed me off with a ton of proprietary Lenovo shit to deal with and I return it in th

Not sure if anyone out there is working on this, don't see any on huggingface to try out. It might be too new at the moment, but it would be cool to see the difference between a tiny model trained wit

The first generation of our 150M model has just been released Its performance is similar to that of GPT2-Small The benchmarks: PIQA: 62.24% Hellaswag: 32.20% Arc-Easy: 44.91% Arc-Challenge: 25.00% Ari

A few people here mentioned interest in a way to test their custom Pi setups, so I figured I’d drop this here: RoastMyHarness The basic idea is a small engine that sets up an environment to run DeepSW

Don't upvote, just looking for CPU ram options for running qwen flash next, and maybe larger models (so maybe between 128 and 256gb total ram) with an existing cmp 170hx. Use case is agentic coding so

VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39

I gave DS V4.1 Flash an HLE problem with a bash tool + 2 hours. Hour 1: it wrote three MILP solvers. (225,200) Hour 2: it downloaded the HLE dataset from Hugging Face, found the question, read the ans

It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram fo

Since we know Qwen 3.8 27B thinks quite long, but gives at least a good one-shot result where you can leave it to do everything on it own, how does it compare to the older series of models for very si

It’s always bothered me that after fine-tuning a model for a project, there isn’t a particularly easy way to host it without either running it locally and keeping a GPU on 24/7 or paying for an entire

Using the RTX3090 on a linux machine I built for it and running Qwen 3.8 27B getting average 100t/s compared to my MacBook 20t/s I think I can finally get rid of my Claude subscription, this is good e

from internlm: We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre

I have a full model, it's ready to train. It's ~9b parameters. 9.4b to be more exact. That includes a 1/2/3 Engram table, Moonshot's AttnRes modeling, and RoPE / NoPE layering at 3:1 as more or less v

my question about LLM performance We see a lot of posts about token prediction, token generation per second, etc. But is it really the metric? I can see that DeepSeek V4 Flash 0731 (with DSPark; mac s

I'll start by saying I'm not talking about the models themselves, I'm aware that I can't come close to something like Fable's intelligence locally. Just wanted to get that out of the way. Basically. O

As a SWE I have ultimate belief that having a irreplaceable subscription on OpenAI and Anthropic models is actually against my beliefs (I'm not fond of becoming a digital slave and being unable to do

This is an evolution on top of Raymond's KV cache streaming fork - all credits to what enabled this goes to him. The basic idea behind what he enabled was a pool of memory in VRAM that is used differe

> 'make an svg of a frog playing on a chello on the back of a whale with carribean island in the back.' interestingly the svg looks different in the OpenWebUi preview then when looked at in preview (o

Lately because of the current hardware shortage, unfortunately or fortunately, we can’t just throw infinite cloud compute at our problems, but we’re forced to actually care about what’s happening unde

The entire page of the NY Times today above the fold absent one article is AI (the models are just too strong/too dangerous, must be regulated). They forgot to include 'Sponsored by OpenAI' at the end

What I have: - CPU: EPYC 7551 (32c/64T, Zen 1) - Board: Supermicro H11SSL-i (SP3), Rev 2.0 - RAM: 128 GB DDR4-2133 (all 8 channels full) - GPU: 2x RTX 3090 (48 GB total, PCIe 3.0) - 1500 W PSU What I

Applied science work, from workflow design, data pipeline, results analysis, article/reports writing and data publishing online. 5 projects I did in the past replicated from start to finish. 3x to 4x

I find this new model at HF: 'Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding. Architecture Agnes-3.0-Flash

Q2 is there and Q4 is uploading as I type. Has his github been updated yet? How do you run this? https://huggingface.co/antirez/deepseek-v4.1-flash-gguf/tree/main submitted by /u/Queasy_Asparagus69 [l

Hey everyone! I'm curious to hear from people that use a combination of cloud-based frontier models and local ones for development. I'm planning to set something similar up and wanted to hear about ac

Blog Post : Per-tensor layout maps for GGUF quantization Reddit thread : New tensor type layouts for my GGUF uploads EDIT : Model card has updated things such as Graph, table, text, etc., submitted by

I have a full M5 Pro Macbook Pro with 48GB of RAM, I'm just getting into this local space. Like many of you, the costs of using frontier/cloud models is getting kinda silly. I don't have any insane ne