Sunk Cost sunkcost.ai Data checked 2026-09-03

What hardware do you need to run Gemma 4 E4B?

Gemma 4 E4B at QAT Q4_0 is 5.2 GB of weights, with a context ceiling of 128k tokens. Not yet rated: released after our last ratings pass. Google's quantisation-aware build for small machines; the published weights are already 4-bit.

Cheapest machine that runs itMac mini M6, 16GB at $899
Shortest pay-backStrix Halo Framework Desktop, 32GB — Pays back in 452 years
Fastest of the ones listedMac Studio M5 Ultra, 96GB — 104 tok/s at 32k context
Honest answer on costPays back in 466 years at 500k tokens a day.

How good is it, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 9 (reasoning mode; 7 without), which puts it in the Below every hosted tier band. Fine for simple, well-specified tasks. Noticeably less capable than anything the big labs sell today. Score source. See the whole table.

What it costs either way

Nobody rents Gemma 4 E4B by the token. The closest hosted match, gpt-oss-20b, costs $0.02 per million input tokens and $0.1 per million output (OpenRouter, cheapest active endpoint, checked 2026-09-03). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill.

Machines that run it

MachinePriceSpeed at 32kPay-back
Mac mini M6, 16GB $899 13 tok/s estimated Pays back in 466 years Run the numbers
Strix Halo Framework Desktop, 32GB $1,269 41 tok/s estimated Pays back in 452 years Run the numbers
MacBook Air M5 (13-inch), 16GB $1,299 13 tok/s estimated Pays back in 674 years Run the numbers
MacBook Pro M5 (14-inch), 16GB $1,999 13 tok/s estimated Pays back in 1,036 years Run the numbers
Mac Studio M5 Max, 36GB $2,499 40 tok/s estimated Pays back in 958 years Run the numbers
DGX Spark GB10 Grace Blackwell, 128GB $4,699 51 tok/s estimated Pays back in 1,571 years Run the numbers

One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration.

The specifics

Parameters
8B, of which 4.5B are active per token
Quantisation
QAT Q4_0
Weights on disk
5.2 GB
KV cache
0.6 GB at 32k context — Only the first 24 layers hold a cache at all: the config shares KV across the last 18, which have no key/value projections. Of those 24, four grow with the context and twenty stop at a 512-token window.
Maximum context
128k tokens (128k)
Licence
Apache 2.0
Sources
source 1