Models
Gemma 4 Just Dropped: What Local AI Builders Need to Know
Google's Gemma 4 is here -- dense and MoE variants, Apache 2.0, multimodal with vision and audio. VRAM requirements, benchmarks, and how it compares to Qwen 3.5.
Qwen 3.5 Small Models: The 9B Beats Last-Gen 30B — Here's What Matters for Local AI
Alibaba's Qwen 3.5 drops 4 small models (0.8B to 9B) — all natively multimodal, 262K context, Apache 2.0. The 9B beats Qwen3-30B on reasoning and destroys GPT-5-Nano on vision. VRAM tables and what to run.
Best 8GB GPU Model: How to Set Up Qwen 3.5 9B (Step by Step)
Qwen 3.5 9B fits in 6.6GB and beats models 3x its size. Complete setup with Ollama, benchmarks, and real-world testing on RTX 3060 and 4060.
DeepSeek V4: Everything We Know Before It Drops
DeepSeek V4 launches next week with native image and video generation, 1M context, and rumored 1T MoE params with only 32B active. Here's what local AI builders need to know and how to prepare.
Best Qwen 3.5 Models Ranked: Every Size, Every GPU, Every Quant
Complete ranking of all Qwen 3.5 models from 0.8B to 397B. VRAM requirements, speed benchmarks, and which model to pick for your hardware.
Qwen 3.5 Locally — 27B vs 35B-A3B vs 122B, Which Model Fits Your GPU
Qwen 3.5 27B dense vs 35B-A3B MoE vs 122B-A10B compared for local inference. VRAM tables, tok/s benchmarks on RTX 3090 and Mac, thinking mode setup, and which to pick for your hardware.
LiquidAI LFM2: The First Hybrid Model Built for Your Hardware
LFM2-24B-A2B runs at 112 tok/s on CPU with only 2.3B active params. Not a transformer. GGUF files from 13.5GB, Ollama and llama.cpp setup, and where it beats Qwen.
Best Way to Run Qwen 3.5 on Mac: MLX vs Ollama Speed Test
MLX runs Qwen 3.5 up to 2x faster than Ollama on Apple Silicon. Head-to-head benchmarks on M1 through M4, with setup instructions for both.
The Benchmarks Lie: Why LLM Scores Don't Predict Real-World Performance
MMLU scores drop 14-17 points when contamination is removed. HumanEval is saturated at 94%. Models trained on the test set. Here's what to measure instead.
RWKV-7: Infinite Context, Zero KV Cache — The Local-First Architecture
RWKV-7 uses O(1) memory per token. Context length doesn't increase VRAM. At all. 16 tok/s on a Raspberry Pi. Here's why it matters for local AI and how to run it.
Distilled vs Frontier Models for Local AI — What You're Actually Getting
That local model you love was probably trained on stolen outputs from Claude or GPT. Here's what distillation actually does to a model's reasoning, where it breaks, and why it matters most for agentic work.
Best Qwen 3.5 Setup: Which Model Fits Your GPU (Complete Cheat Sheet)
Pick the right Qwen 3.5 model for your hardware. Covers 0.8B through 397B with VRAM requirements, quant recommendations, and benchmarks for every GPU tier.
nanollama: Train Your Own Llama 3 From Scratch on Custom Data
Pretrain Llama 3 architecture models from raw text, export to GGUF, and run with llama.cpp. Forked from Karpathy's nanochat. 46M to 7B parameters.
MoE Models Explained: Why Mixtral Uses 46B Parameters But Runs Like 13B
Mixture of Experts explained for local AI — why MoE models run fast but still need full VRAM. Mixtral, DeepSeek V3, DBRX compared with dense model alternatives.
Qwen vs Llama vs Mistral: Which Model Family Should You Build On?
Qwen has 201 languages and a model for every task. Llama has the biggest community. Mistral pioneered efficient MoE. Decision framework for choosing your model family in 2026.
Ouro-2.6B-Thinking: ByteDance's Looped Model That Punches Like an 8B
Ouro-2.6B loops through the same transformer blocks 4 times to match 8B models at 2.6B parameters. Under 2GB at Q4. How the architecture works and why it matters.
Mixtral VRAM Requirements: 8x7B and 8x22B at Every Quantization Level
Mixtral 8x7B has 46.7B params but only 12.9B activate per token. You still need VRAM for all 46.7B. Exact VRAM for every quant from Q2 to FP16.
Qwen3 Complete Guide: Every Model from 0.6B to 235B
Qwen3 is the best open model family for budget local AI. Dense models from 0.6B to 32B, MoE models that punch above their weight, and a /think toggle no one else has.
Llama 4 vs Qwen3 vs DeepSeek V3.2: Which to Run Locally in 2026
Llama 4 needs 55GB. DeepSeek V3.2 needs 350GB. Qwen3 runs on 8GB. Here's who wins at each VRAM tier and use case for local AI in 2026.
Llama 4 Guide: Running Scout and Maverick Locally
Complete Llama 4 guide for local AI — Scout (109B MoE, 17B active) and Maverick (400B). VRAM requirements, Ollama setup, benchmarks, and honest hardware reality check.
GPT-OSS Guide: OpenAI's First Open Model for Local AI
GPT-OSS 20B is OpenAI's first open-weight model. MoE with 3.6B active params, MXFP4 at 13GB, 128K context, Apache 2.0. Here's how to run it.
DeepSeek V3.2 Guide: What Changed and How to Run It Locally
DeepSeek V3.2 competes with GPT-5 on benchmarks. The full model needs 350GB+ VRAM. But the R1 distills run on a $200 used GPU — and they're shockingly good.
CodeLlama vs DeepSeek Coder vs Qwen Coder: Best Local Coding Models Compared
CodeLlama vs DeepSeek Coder vs Qwen Coder vs Codestral benchmarked: HumanEval scores, VRAM per quant, and speed tests. Qwen 7B beats CodeLlama 70B.
Phi Models Guide: Microsoft's Small but Mighty LLMs
Phi-4 14B scores 84.8% on MMLU — matching models 5x its size — and fits on a 12GB GPU at Q4. The full Phi lineup from 3.8B to 14B with VRAM needs, benchmarks, and honest weaknesses.
Gemma Models Guide: Google's Lightweight Local LLMs
Gemma 3 27B beats Gemini 1.5 Pro on benchmarks and runs on a single GPU. The 4B outperforms Gemma 2 27B. Full lineup from 1B to 27B with VRAM needs, speeds, and honest comparisons.
Mixtral 8x7B & 8x22B VRAM Requirements
Mixtral 8x7B and 8x22B VRAM requirements at every quantization level. Exact numbers from Q2 to FP16, GPU recommendations, and KV cache impact explained.
Are Mistral Models Still Worth Running? Only Nemo 12B (Here's Why)
Mistral led local AI in 2024. In 2026, Qwen 3 and Llama 3 have passed them on most benchmarks. The exception: Mistral Nemo 12B with 128K context still earns its slot. What's worth running, what's been replaced, and when to pick Mistral over the competition.
Llama 3 Guide: Every Size from 1B to 405B
Complete Llama 3 guide covering every model from 1B to 405B. VRAM requirements, Ollama setup, benchmarks vs Qwen 3, and which size fits your hardware.
DeepSeek Models Guide: R1, V3, and Coder
Complete DeepSeek models guide covering R1, V3, and Coder locally. Which distilled R1 to pick for your GPU, VRAM requirements, and benchmarks vs Qwen 3.
Best Qwen Models Ranked: Which to Run Locally
Complete Qwen models guide covering Qwen 3.5, Qwen 3, Qwen 2.5 Coder, and Qwen-VL. VRAM requirements, Ollama setup, Gated DeltaNet architecture, and benchmarks vs Llama and DeepSeek.
Best Local LLMs for Math & Reasoning: What Actually Works
The best local LLMs for math and reasoning tasks, ranked by VRAM tier. AIME and MATH benchmarks for DeepSeek R1, Qwen 3 thinking, and Phi-4-reasoning.
Best Local LLMs for Chat & Conversation
The best local LLMs for chat and conversation in 2026. Picks for every VRAM tier from 8GB to 24GB, with Ollama commands to start chatting immediately.
Model Formats Explained: GGUF vs GPTQ vs AWQ vs EXL2
GGUF vs GPTQ vs AWQ vs EXL2 model formats explained. Learn what each format does, which tools support them, and how to choose the right one for your GPU.
Best Local LLMs for Writing & Creative Work
Qwen 2.5 32B on 24GB VRAM is the sweet spot for fiction and long-form. On 8GB, Nous Hermes 3 8B punches above its weight. Model picks for every tier and writing task.
Best Models Under 3B: Small LLMs That Work
The best models under 3B parameters for laptops, old GPUs, Raspberry Pi, and phones. What works, what doesn't, and which tiny LLM to pick for your use case.
Best Local Coding Models Ranked: Every VRAM Tier, Every Benchmark (2026)
The best local LLMs for coding in 2026, ranked by VRAM tier. Benchmarks, editor setup, and practical recommendations for developers replacing Copilot.
Quantization Explained: What It Means for Local AI
Q4_K_M shrinks a 7B model from 14GB to ~4GB while keeping 90-95% quality. What every quantization format means, how much VRAM each saves, and which to pick for your GPU.