Local LLM VRAM Requirements in 2026, Tier by Tier

The maths is simpler than the forum threads suggest. Take the parameter count in billions, multiply by 0.6, and that is roughly the VRAM in gigabytes you need at 4-bit quantisation. An 8B model lands around 4 to 5 GB. A 14B sits near 9 GB at Q4_K_M. A 70B needs about 40 GB, which is more than a 24 GB RTX 4090 holds on its own.

Then add 10 to 20 percent on top for the KV cache, and more than that if you are pushing past 100K token contexts. The cache grows with context length, and it is the thing that quietly kills a setup that looked fine on paper.

Local LLM VRAM requirements by tier

• 4 GB, entry. 4B class models such as Qwen 3.5 4B. Fine for summarising, classification and simple chat. Not a reasoning machine.
• 6 to 8 GB, the sweet spot. Llama 3.1 8B at Q4 fits with room to spare. Most people should start here, because the quality jump from 4B to 8B is larger than the jump from 8B to 14B.
• 12 to 16 GB, comfortable. 26B to 30B models. Noticeably better at multi-step instructions and code.
• 16 to 24 GB, power. 32B models with a real context window. This is where a local model stops feeling like a demo.
• 40 GB and up, 70B class. Either a 32 GB card with aggressive quantisation and offloading, or a machine with a lot of unified memory.

Minimum viable build: 16 GB of system RAM, a modern CPU, and either 6 GB or more of VRAM or an Apple Silicon Mac.

Which hardware gets you there

The RTX 3090, 4090 and 5090 remain the value picks at 24 to 32 GB, at a fraction of data centre card pricing. The 5090's 32 GB of GDDR7 gives enough headroom for a quantised 70B with a context window you would actually use. Cards in this class are listed on Amazon, though stock and pricing move week to week.

Apple Silicon works differently. Unified memory means system RAM doubles as VRAM, so an M5 Max with 64 GB runs models that would otherwise need a data centre card. A Mac mini with 24 GB of unified memory is one of the cheapest ways into the 12 to 16 GB tier without building anything.

If you are running partly on CPU or offloading layers, system RAM matters too. A 32 GB DDR5 kit is the sensible floor for a machine that will also hold a model in memory.

Why 2026 pricing is probably the cheap window

Memory is getting more expensive, not less. HBM3E has moved up around 20 percent and Samsung foundry wafers are up 10 to 15 percent. Foundry increases historically reach shelves on a nine to eighteen month lag, which means the card and RAM prices you are looking at now are more likely a floor than a peak. If a local AI build is on your list for next year, the arithmetic favours doing it sooner.

For the background on why memory is behaving this way, see our explainers on HBM vs GDDR vs DDR and High Bandwidth Flash. If you want to change how a local model behaves once it is running, abliteration is the technique to read up on.

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