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 GPUs and Macs that hold it.
Llama 3.3 70B Instruct at 8K context
| Quantization | Weights | KV cache + overhead | Total | Smallest hardware that holds it |
|---|---|---|---|---|
Q2_K |
28.2 GB | 3.3 GB | 31.5 GB | Apple M4 Pro (48 GB), Apple M1 Max (64 GB), Apple M4 Max (64 GB) |
Q3_K_M |
34.6 GB | 3.3 GB | 37.9 GB | Apple M1 Max (64 GB), Apple M4 Max (64 GB), NVIDIA RTX PRO 6000 Blackwell |
Q4_K_M |
43.1 GB | 3.3 GB | 46.4 GB | NVIDIA RTX PRO 6000 Blackwell, AMD Ryzen AI Max+ 395 (128 GB), Apple M4 Max (128 GB) |
Q5_K_M |
50.1 GB | 3.3 GB | 53.4 GB | NVIDIA RTX PRO 6000 Blackwell, AMD Ryzen AI Max+ 395 (128 GB), Apple M4 Max (128 GB) |
Q6_K |
57.9 GB | 3.3 GB | 61.2 GB | NVIDIA RTX PRO 6000 Blackwell, AMD Ryzen AI Max+ 395 (128 GB), Apple M4 Max (128 GB) |
Q8_0 |
74.8 GB | 3.3 GB | 78.1 GB | NVIDIA RTX PRO 6000 Blackwell, AMD Ryzen AI Max+ 395 (128 GB), Apple M4 Max (128 GB) |
F16 |
141 GB | 3.3 GB | 144 GB | Apple M3 Ultra (256 GB), Apple M3 Ultra (512 GB) |
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 what your GPU or Mac 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.