Hardware · Memory

How much memory does a model need?

Pick an LLM and a context length. We add up the weights at every quantization, the KV cache that grows with context, and runtime overhead, then show the smallest systems and devices that hold it.

Model

Context length

Qwen3 Coder 480B-A35B at 8K context

Quantization	Weights	KV cache + overhead	Total	Smallest hardware that holds it
`Q2_K` Low	192 GB	2.7 GB	195 GB	Apple M3 Ultra (512 GB) 12 in all
`Q3_K_M` Low	235 GB	2.7 GB	238 GB	Apple M3 Ultra (512 GB) 7 in all
`Q4_K_M` Good	293 GB	2.7 GB	296 GB	Apple M3 Ultra (512 GB) 6 in all
`Q5_K_M` High	341 GB	2.7 GB	344 GB	Apple M3 Ultra (512 GB) 6 in all
`Q6_K` High	394 GB	2.7 GB	396 GB	AMD EPYC 7002 / 7003 series NAS (512 GB) Intel Xeon D-2700 series NAS (512 GB) Intel Xeon Silver (4100 to 4300 series) NAS (512 GB) 4 in all
`Q8_0` Very high	509 GB	2.7 GB	512 GB	Nothing in the catalog holds this on one machine.
`F16` Full	960 GB	2.7 GB	963 GB	Nothing in the catalog holds this on one machine.

Hardware is listed when its usable memory is at least 8% above the total. Macs count roughly two-thirds to three-quarters of unified memory as usable. Each link opens the full “can it run” breakdown with speed.

The rule of thumb

1 · WeightsParameters × bytes eachA 70B model at 4 bits per weight is about 70 × 0.6 ≈ 42 GB. Lower quantization shrinks it and costs some quality.

2 · KV cacheGrows with contextEvery token you keep in context stores keys and values for each layer. Doubling context doubles this part, so long documents cost real memory.

3 · OverheadLeave headroomThe runtime, the operating system and other apps need memory too. Aim for a total at least 10% under the memory your GPU or device can give a model.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.