Runyard is a free hardware-aware AI model browser. You enter your CPU, GPU, and VRAM and it instantly shows every local LLM that will run on your machine, ranked by speed and quality.

How much VRAM do I need to run local LLMs?

8GB of VRAM runs 7B models like Llama 3.1 8B and Mistral 7B at Q4 quantization. 16GB unlocks 13B models. 24GB lets you run Mixtral 8x7B and Llama 3 70B at lower quantization.

What is the best local LLM for my GPU?

Use Runyard at www.runyard.dev — enter your GPU and VRAM and the Model Radar will rank every compatible LLM for your exact hardware, showing estimated tokens per second for each model.

Can I run Llama 3 locally?

Yes. Llama 3.1 8B at Q4 runs on any 8GB VRAM GPU. Llama 3.1 70B needs around 40GB VRAM at Q4, or an Apple Silicon Mac with 64GB+ unified memory.

RUNYARD.DEV / BLOG

Latest Updates

Guides, deep-dives, and news on local AI models, hardware, and the Runyard platform.

June 18, 2026comparison

Q4_K_M vs Q5_K_M vs Q6_K vs Q8_0: How Much Quality Do You Lose?

Quantizing Llama-3.1-8B to Q4_K_M cuts the file from 14.96 GB to 4.57 GB — a 69% reduction — while perplexity rises just 0.24 points and MMLU drops about one point. Here is where each GGUF level actually breaks.

Cloud GPU Pricing in India 2026 — Vast.ai vs RunPod vs Salad vs E2E vs Yotta

Indian devs pay ₹40-180/hr for an A100 depending on provider. We benchmark the 5 major options on price, region latency, billing flexibility, and tax handling.

Read more →8 min read

April 29, 2026deep-dive

How Local LLM Inference Actually Works: Loading, Memory, and Quantization Explained

You downloaded a model — now what? A plain-English walkthrough of how inference engines load weights, manage memory with MMAP, and why quantization format matters more than programming language.

Run Gemma 4 Locally for Free: Google's Best Open AI Model on Your Own Machine

Google's Gemma 4 runs completely offline — no subscriptions, no data leaving your machine. Here's exactly how to set it up with Ollama in under 10 minutes.

Read more →10 min read

March 31, 2026hardware

How Much of a Boost Does TurboQuant Actually Give Your GPU?

TurboQuant compresses the KV cache 4–5×. But the real question is: how does that change the composite score for your specific GPU and model? We ran the numbers. The answer depends on your VRAM.

Introducing Runyard Compare: Head-to-Head GPU Benchmarks with TurboQuant

Runyard just shipped a new Compare page — pick any two devices, toggle TurboQuant, and instantly see which GPU wins for every LLM. Here's what's new and why TurboQuant changes the result.

How to Run TurboQuant: We Tested Google's Algorithm on a Consumer GPU

We built a ground-up Python implementation of TurboQuant and tested it on Qwen 2.5 3B running on an RTX 3060. 5.3x KV cache compression. 99.5% attention fidelity at 3-bit. Here's exactly what happened.

Google TurboQuant: Your Local LLM Just Got a 4x Context Window Upgrade

Google's TurboQuant research (Zandieh et al., ICLR 2026) cuts KV cache memory by 4x — meaning the 7B model on your GPU can now run 32K context where it used to max out at 8K. Here's what changes today.

Read more →8 min read

March 27, 2026tutorial

We Built a Hardware MCP Server: Auto-Detect Your GPU and Get the Right LLM

@runyarddev/hw-mcp is a free MCP server that auto-detects your GPU, VRAM, and RAM, then ranks 900+ LLMs by hardware fit — giving any MCP-compatible AI agent instant hardware awareness.

Read more →10 min read

March 25, 2026security

LiteLLM Supply Chain Attack: What Happened and How to Fix It

On March 24, 2026, LiteLLM versions 1.82.7 and 1.82.8 were poisoned with a three-stage credential stealer targeting API keys, SSH keys, cloud credentials, and crypto wallets. Here's exactly what happened and what to do now.

Read more →9 min read

March 25, 2026security

Have You Been Pawned by LiteLLM?

LiteLLM versions 1.82.7 and 1.82.8 contained a credential-stealing backdoor. Paste your pip freeze output into our checker to find out if your machine was compromised — and what to do next.

Can I Run Image Models Locally on My Computer?

Yes — easier than you think. From LLaVA on an 8GB GPU to MiniCPM-V on a MacBook, every local vision model worth running in 2026 and which fits your hardware.

Read more →9 min read

March 25, 2026models

LFM-2.5VL-1.6B: The Vision Model That Runs in Your Browser

LFM-2.5VL-1.6B runs fully in Chrome, Edge, and Firefox via WebGPU — no server, no API key, no data leaving your device. Here's how to use it today.

Read more →10 min read

March 24, 2026local-ai

"Claude Code for Free" via OpenRouter — The Real Truth

"Claude Code for free via OpenRouter" is everywhere on YouTube. It's not a lie — but not the full truth. Here's what you're actually getting and the real free alternative.

Read more →8 min read

March 24, 2026runyard

How to Use Runyard — Step-by-Step Explainer

Runyard shows which AI models fit your hardware and how fast they run. Complete walkthrough of every feature — from entering GPU specs to clicking Analyze.

Find the Best Local LLM for Your PC (2026 Guide)

Stop downloading models that don't fit. Match an open-source LLM to your GPU, RAM, and use case — then run it in minutes with Ollama or LM Studio.

Read more →10 min read

March 18, 2026hardware

How Much VRAM Do You Need to Run Local LLMs?

8GB gets you started, 16GB runs most models comfortably, 24GB+ unlocks the best open-source models. Full breakdown by model size and quantization.

Read more →8 min read

March 15, 2026local-ai

Best Local LLMs for Coding in 2026

DeepSeek Coder V2, Qwen2.5 Coder 32B, and CodeLlama 70B lead the pack — but the right choice depends on your VRAM. Full ranked list with benchmarks.

Read more →9 min read

March 12, 2026comparison

Ollama vs LM Studio: Which Should You Use in 2026?

Ollama is faster to set up, perfect for developers. LM Studio has a polished GUI and better model discovery. The right choice depends on how you work.

What LLMs Can You Run with 8GB VRAM?

An RTX 3070, 4060, or any 8GB GPU can run Llama 3.1 8B, Mistral 7B, DeepSeek Coder 6.7B, and more — all fit comfortably at Q4 quantization.

How to Run Llama 3 Locally: Complete Step-by-Step Guide

Meta's Llama 3.1 is one of the best open-source LLMs. This guide walks you through every step — from picking the right size to running it in minutes.

Read more →10 min read

March 5, 2026comparison

Local LLM vs OpenAI API: The True Cost Comparison (2026)

Local LLMs have upfront hardware costs but near-zero marginal cost. OpenAI API charges per token. We break down exactly when running local wins.

Read more →8 min read

March 1, 2026hardware

Best GPU for Running Local LLMs in 2026

RTX 4090 is the gold standard, RTX 4070 Ti the best value, Apple Silicon the wildcard. Every major GPU ranked for local LLM inference with tok/s data.

Read more →9 min read

February 28, 2026runyard

Introducing Runyard: Find AI Models for Your Hardware

Runyard tells you exactly which AI models run on your computer and how fast. No more guessing, no more downloading 20GB models that don't fit.

Read more →8 min read