{
  "id": 1293033,
  "title": "How Much VRAM Do You Really Need for Local LLMs?",
  "url": "https://urgent.news/2026/08/16/how-much-vram-do-you-really-need-for-local-llms",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-16T15:11:56.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/apeder/how-much-vram-do-you-really-need-for-local-llms-142n"
  },
  "original_language": "en",
  "account": "Determining the appropriate amount of VRAM for running local large language models (LLMs) involves understanding a simple VRAM equation. At its core, VRAM is approximately equal to the number of model parameters multiplied by the number of bits per weight. For example, a 7 billion parameter model using 16-bit precision weights would need around 14 gigabytes of VRAM.\n\nHowever, this metric is often made more complex than it needs to be for local AI usage. A key strategy to make local LLMs feasible on consumer hardware is through quantization, which reduces the precision of the model's weights from higher bit depths like 16-bit to lower bit depths like 4-bit. This lower precision comes with a small trade-off in quality, but it significantly reduces the VRAM needed to store the model.\n\nFor instance, a 7 billion parameter model would need approximately 7 gigabytes of VRAM when quantized to 4-bit format. This makes it much more manageable for mainstream computers. The most common format for quantized models is Q4, which strikes a balance between memory efficiency and acceptable quality, allowing mainstream graphics cards to handle larger models with reasonable performance.\n\nWhen considering popular models, quantization heavily influences whether they can run on a single consumer GPU. For example, a 70 billion parameter model at Q4 precision would need roughly 40 gigabytes of VRAM for just the model weights alone. However, this does not account for additional memory required for context windows, caches, and running the application itself. Therefore, a 70B model might require closer to 70 gigabytes of VRAM when considering practical usage needs.\n\nThe landscape of 2026 hardware provides a range of options. The NVIDIA RTX 5090, for instance, offers 32 gigabytes of GDDR7 memory and high bandwidth, capable of handling models like 7B, 14B, and even 32B at Q4 precision. On the other hand, unified memory systems like the Mac Studio M5 Ultra provide larger shared memory for GPU and CPU, potentially allowing for the execution of 120 billion parameter models natively, which would be impossible on discrete GPUs.\n\nFor those aiming to run larger models, such as 70 billion parameters or more, there are two primary routes. One involves using multi-GPU setups, where multiple high-end GPUs are combined to provide the necessary VRAM. This approach requires coordination, high-wattage power supplies, and specialized runtime software to manage the distributed model. The other route is to use specialized desktop AI appliances like the NVIDIA DGX Spark, which offers substantial unified memory capacity. This represents a significant investment but provides an environment where even massive models can be run locally. The choice between these options depends on the specific needs for model scale versus the balance between cost, complexity, and performance.",
  "summary": "The VRAM equation explained: quantized model sizes, real 2026 GPU options, and the 'can I run it?' answer for any local model. Can You Run It? The Question Every Gamer Already Knows Every PC gamer knows the feeling: you open a store page, scroll to the system requirements, and hold your breath. \"Can I run it?\" The GPU shader count, the RAM floor, the dreaded minimum versus recommended specs.…",
  "key_points": [
    "VRAM needed for 7B parameter model at 16-bit = 14GB",
    "Quantization to 4-bit reduces 7B model VRAM to 7GB",
    "70B model Q4 precision needs ~40GB VRAM, plus additional memory"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}