AI Wiki — Knowledge Base

Would a 2nd hand custom built 2080 Ti 22GB vram be worth it? How usable would it be with ComfyUI? Or maybe even 2 pcs with NVLink? Can large models, like Wan2.2 be split with NVLink like it's 44GB, or will it always be 22GB for 1 model, and 22GB for another model (encoders, CLIP, anything else)?

This Reddit post discusses the viability of using second-hand custom-built RTX 2080 Ti GPUs with 22GB VRAM for running Stable Diffusion models in ComfyUI, including whether two cards could be linked v

local-air-stablediffusion

4 May 2026

Search: “models”

I benchmarked Qwen 3.8 27B on my M4 Max — here's what I learned about local models, context, and coding performance

A collection of small domain-specific benchmarks for local models (30+ and growing)

Detecting hallucinations in local models without eating VRAM: What we learned testing 1.5B to 120B models

Is there a point where models just cannot get any smaller without losing intelligence?

23 Gemma4-E4B models compared with abliterlitics: the most downloaded one is also the most broken

Thank you :) Swift Models hit 2.2 million+ downloads / Early Access to New Models, Free Compute for Researchers

Aplomb 1: open-weights 5.3B decision model, 1M context, text/image/video/audio in one request, #1 among 4B models on the Decision Index

Converting dense models into Mixture-of-Experts

Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights)

Run Claude Code on Ollama models with per-model context scaling and cost tracking

DeepSeek-V4-Flash-0731: Models you can run locally now have the intelligence score of the top frontier model from March 2026

Would a 2nd hand custom built 2080 Ti 22GB vram be worth it? How usable would it be with ComfyUI? Or maybe even 2 pcs with NVLink? Can large models, like Wan2.2 be split with NVLink like it's 44GB, or will it always be 22GB for 1 model, and 22GB for another model (encoders, CLIP, anything else)?

llama.cpp optimizations for specific hardware and models

Kimi K3 in the next few hours. Deepseek V4 GA later in the week. New Liquid models. New Mistral models sometime this month. And some rumours suggest GLM 5.5 is coming in August. Openweight AI is eating good.

All these new models landing this year but Flux Klein 9b FP8 has spoiled me. All I care about now is whether a new model can edit and be used on an 8GB GPU.

Running decision models: Picking quant, VRAM usage

Qwen-family LLMs are quietly becoming the backbone of modern audio models; One chart for the architectures of 100+ audio models

Hoping for Optimized Smarter Upcoming Models .... Like DeepSeek-V4.1-Flash( KVCache + Engram) in Small/Medium/Big sizes

Concerning 'humanlike models' and chatbot RP in general...

[RELEASE] - SupraGDN-5M - a tiny GatedDeltaNet model competing with other community while models trained on MUCH less data!

worldproof: diagnosing where world-model predictions break and a measurement of when pixel metrics stop being able to rank models at all [P]

Experience sharing: How do you use your local models and for what kind of tasks?

Why Anthropic's battle is meant to poison the wells of open weight models, in 3 steps.

I made a way to migrate between embedding models without re-embedding your entire corpus

Making small local models actually useful for coding

Quartermaster 1.7 update now lets you run llama.cpp's new decision models (/v1/systemone) with a playground tab

2 months (meme-d) research, Do People Notice the Difference Between AI Models?

Running an AI SaaS against local Ollama models — what actually breaks when you swap the provider

Nathan Lambert's written Congressional testimony on the state of open models - Chinese open-weight downloads now 2x America's, >80% of OpenRouter open-model usage

I benchmarked 9 open models on spotting fake sources during agentic search (DeepSeek V4, Qwen 3.8, Nemotron 3 Ultra)

Anthropic “our models hacked three different external companies, months before OpenAI’s model was able to do the same'

GPT-6.1 Sol looped 'leak' hints at nested models serving architecture

The models are not trying to escape. They are trying to finish the task.

Zer0Fit - Zero-shot predictions, classifications, and regressions using Google ML research models running locally as a dockerized MCP

I stopped using the smartest AI models. Programming got faster and cheaper.

I built an LLM benchmark harness that lets you browse and compare how models answered each question

I released Inflect v2: two ultra-tiny complete TTS models under 4M and 10M parameters

R9V Update: Created and adopted KVA projections based on Deepseek V4.1 Flash + HySparse2/MiMo-V3 for Qwen3.8 Flash Next. This is a game changer for models that don't natively implement it. 1.45-1.85x speedup in prefill to 3k+ at a small deficit to perplexity. [2x R9700, 128GB DDR5]

No, Engrams won't let you run 1T models locally. It does something even better.

[Paper] ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning

With release of Deepseek V4 I wanted see how the model sizes are trending over time. The trend is that by this time next year, we probably will have Opus 4.5 level models on consumer grade laptops!

100B Models on Cheap Hardware: how realistic and limitations

[Paper] Statistically-Lossless Quantization of Large Language Models

Is anyone using models to describe an image and get a prompt? Is there much difference between Qwen 3.5 9b vs Qwen 3.5 27b, vs gemma 4 27b and another model you use ?

Just another purely open-weight models benchmark

CrofAI 'cheapest inference provider in the world' gets exposed as an OpenRouter wrapper, routing requests to smaller, cheaper models at up to 20x markup. CrofAI responds to Wire Fraud allegations by denying everything, then backtracking, then 3 hours later wiping their entire online presence

Which local models handle structured extraction well? I moved my agent's memory into a folder (BYO model) and want to run it on Ollama instead of Sonnet

All currently popular local models in one table + Opus 4.8 results