We Checked Every 'Runs on 8GB' Claim. It Breaks at 9B

'Runs on 8GB' is the most repeated claim in local AI and almost nobody checks it. We ran every common model size through the same arithmetic to find where it stops being true.

It stops at 9B, and it stops by 0.3 GB.

An 8 GB card gives about 7.2 GB usable once drivers and the desktop take their share. At Q4_K_M with an 8K context:

• ▸1B — needs 2.1 GB. Fits easily.
• ▸3B — needs 3.5 GB. Fits easily.
• ▸7B — needs 6.2 GB. Fits, with a gigabyte to spare.
• ▸8B — needs 6.9 GB. Fits, with 0.3 GB to spare.
• ▸9B — needs 7.5 GB. Misses by 0.3 GB.
• ▸12B — needs 9.5 GB. Not close.
• ▸14B — needs 10.7 GB. Not close.

When someone says a 12B runs on their 8 GB card, they are typically doing one of three things, and none of them is what the reader assumes:

• ▸Running at Q3_K_M or lower, where a 12B needs 8.0 GB — still over, but close enough that a short context sneaks it in.
• ▸Running with partial CPU offload, at a few tokens per second.
• ▸Running with a 2K context, which fits but is too short for real work.

A 7B or 8B model at Q4_K_M, with the context set around 4K to 8K. That is a genuinely useful setup — it covers chat, code completion and summarising, and it runs at full speed with everything resident.

The upgrade that changes things is not 12 GB, which tops out at 14B. It is 24 GB, which reaches 27B. Between those two the model tier moves once.

Every figure here is computed the same way the calculators do it: weights are parameters times bits-per-weight divided by eight, plus a KV-cache term for your context, plus about a gigabyte of runtime overhead. Usable memory is roughly 90% of nameplate on a discrete card.