Datasets:
How much GPU memory do you need to serve a 70B model?
Short answer for Llama 3.1 70B: about 141 GB for BF16 weights alone, so plan on 2 x 80 GB GPUs as the bare minimum and 4 x 80 GB for comfortable serving. With FP8 it squeezes onto one 80 GB GPU for light traffic; with 4-bit it fits on one 80 GB GPU (or two 48 GB GPUs) with useful KV space. Here is the full calculation so you can redo it for any model.
Step 1: weights
Llama 3.1 70B has 70.6B parameters.
- BF16: 70.6B x 2 bytes = 141.2 GB (131.5 GiB)
- FP8/INT8: 70.6B x 1 = 70.6 GB (65.8 GiB)
- INT4: 70.6B x 0.5 = 35.3 GB, plus a few percent for quantization scales
Step 2: KV cache per token
From the model's config: 80 layers, 8 KV heads (grouped-query attention), head_dim 128.
KV per token = 2 x 80 x 8 x 128 x 2 bytes = 327,680 bytes = 320 KiB (BF16), or 160 KiB with an FP8 KV cache.
Per sequence:
- 4,096 tokens: 1.25 GiB
- 8,192 tokens: 2.5 GiB
- 32,768 tokens: 10 GiB
- 128K tokens (the model's maximum, 131,072): 40 GiB
Step 3: overhead
Engines reserve memory for activations, CUDA graphs, and the CUDA context. Budget roughly 1 to 3 GiB per GPU, and remember engines like vLLM only use a fraction you set (--gpu-memory-utilization, default 0.9).
Step 4: put it together
Usable memory per 80 GB GPU at 0.9 utilization ~= 72 GB = 67 GiB, minus ~2 GiB overhead = ~65 GiB.
| Setup | Usable | Weights | Left for KV | Tokens of KV (BF16) | 8K-token sequences |
|---|---|---|---|---|---|
| 1 x 80 GB, BF16 | 65 GiB | 131.5 GiB | does not fit | - | - |
| 2 x 80 GB, BF16 | 130 GiB | 131.5 GiB | does not fit at 0.9 | - | - |
| 2 x 80 GB, BF16, util 0.95 | ~138 GiB | 131.5 GiB | ~6 GiB | ~20K | ~2 |
| 4 x 80 GB, BF16 | ~260 GiB | 131.5 GiB | ~129 GiB | ~420K | ~51 |
| 1 x 80 GB, FP8 weights + FP8 KV | ~65 GiB | 65.8 GiB | does not fit at 0.9 | - | - |
| 1 x 80 GB, INT4 weights | ~65 GiB | ~34 GiB | ~31 GiB | ~100K | ~12 |
(Numbers from the formulas above; "8K-token sequences" = KV left / 2.5 GiB.) The FP8 row shows why "70B in FP8 fits on one H100" is only just true: you need to raise utilization close to 1.0 and keep context short. A 141 GB H200 holds FP8 70B comfortably (~50 GiB left for KV at 0.9 utilization).
Step 5: choose by workload
- Few users, short prompts, budget-limited: INT4 on one 80 GB GPU.
- Production chat at moderate scale: BF16 or FP8 with tensor parallelism across 4 GPUs, or FP8 on 2 GPUs.
- Long context (32K+) for many users: KV dominates; use FP8 KV cache, more GPUs, and prefix caching.
Do it for any model
Read config.json and use:
- weights = params x bytes per param
- KV/token = 2 x num_hidden_layers x num_key_value_heads x (hidden_size / num_attention_heads) x bytes
- concurrent tokens = (usable memory - weights) / KV per token
Our zero-dependency calculator does this for you: kv_calc.py.
If you want to go deeper, the free Foundations course at Inference Engineering Academy covers these topics with browser labs. Disclosure: written by the academy team.