vram

Single-GPU LLM fine-tuning preflight: memory estimates, runnable LoRA/QLoRA recipes, and local measurements.

Use your NVIDIA GPU's VRAM as swap space on Linux. Built for laptops with soldered memory and no upgrade path. If you have an RTX card sitting there with 8GB of VRAM and you're getting swapped to SSD, this puts that VRAM to work

🌆 Utilities for handling monitors, resolutions, wallpapers and timed wallpapers + display available VRAM

MacOS menu‑bar utility to adjust Apple Silicon GPU VRAM allocation

Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health. Built-in read-only MCP server so AI agents can explore it too.

Terminal monitor for NVIDIA GPUs on Linux and DGX Spark: live GPU usage, memory, power and temperature, each process's GPU memory in its own color, a record of every GPU job from start to end with how it ended, and history that survives restarts.

Social AI Agent Blueprint. Powered by vram.ai

Virtual Machine with a 240x160 VRAM display.

First open-source TurboQuant KV cache compression for LLM inference. Drop-in for HuggingFace. pip install turboquant.

Can my GPU run this? Pick a GPU to get 3 fitting AI models, quantization, VRAM, speed ranges, and run commands. 한국어·English.

Local AI for your whole house: chat, images and voice on your own GPU. Computes each model's flags, fits them to your VRAM, and hot-swaps them behind one OpenAI- and Anthropic-compatible API.

Compute-for-memory research lab for running larger LLMs on memory-constrained consumer GPUs. Official website exvram.pro

Hardware-accelerated Linux kernel block driver and multi-tier memory cascade (ZRAM ➔ PCIe VRAM ➔ NVMe). Opportunistically leases idle GPU memory as a zero-allocation, revocable swap cache with sub-millisecond latency, zero SSD wear, and crash-safety on Linux & WSL2.

GamePause for Local AI: a Windows tray app that pauses LM Studio, Ollama and other local AI servers while you game, and restores them afterward.

Calculate VRAM requirements for running Large Language Models (LLMs) and recommends suitable GPUs based on your needs

Rust block device in userspace

Which local LLMs can your computer run? Hardware-aware CLI, TUI and GUI: yes/slow/no verdicts and tok/s from your GPU VRAM and RAM, then verified installs via Ollama, llama.cpp, MLX and LM Studio. Native Rust.

Detect and work around silently-corrupting VRAM on consumer GPUs. No ECC, no page retirement, no error - the driver just returns wrong data.

The local-inference observability layer for your terminal: live tokens/sec, VRAM-spill & throttle warnings, the GPU metrics nvidia-smi can't show. Also a gorgeous htop/btop-class system monitor. Rust, zero deps.

I Ran Qwen 3.8 27B on Just 4GB VRAM! (FreeToken Tutorial) - Edge-native MoE serving engine for local LLMs on low VRAM GPUs