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.

Llama 3.1 Nemotron Ultra 253B v1 at 8K context

Quantization Weights KV cache + overhead Total Smallest hardware that holds it
Q2_K Low 101 GB 4.8 GB 106 GB 29 in all
Q3_K_M Low 124 GB 4.8 GB 129 GB 25 in all
Q4_K_M Good 155 GB 4.8 GB 159 GB 20 in all
Q5_K_M High 180 GB 4.8 GB 185 GB 12 in all
Q6_K High 208 GB 4.8 GB 213 GB 12 in all
Q8_0 Very high 269 GB 4.8 GB 273 GB 6 in all
F16 Full 507 GB 4.8 GB 512 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.

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