Llama 3 70B VRAM Calculator
Estimate GPU memory to run Llama 3 70B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Llama 3 70B is the workhorse open-weights model for multi-GPU nodes: FP16 weights alone are ~141 GB, so single-GPU use is only practical at 4-bit on an 80 GB card.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Llama 3 70B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Llama 3 70B locally or in production. It sums the three real costs of inference: the model weights at your chosen precision (FP16, INT8 or INT4), the key-value attention cache that grows with context length and concurrent sequences, and ~10% runtime overhead for CUDA buffers and fragmentation. Llama 3 70B is the workhorse open-weights model for multi-GPU nodes: FP16 weights alone are ~141 GB, so single-GPU use is only practical at 4-bit on an 80 GB card. Use the precision and context sliders to find the cheapest GPU that actually fits your workload instead of guessing from the parameter count alone.
How to use Llama 3 70B VRAM Calculator
- 1Enter your values into Llama 3 70B VRAM Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Llama 3 70B VRAM Calculator?
- ✓Computes Llama 3 70B VRAM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: VRAM ≈ 1.1 × (P×bytes(precision) + 2×layers×kv_heads×head_dim×ctx×batch×bytes(kv)) — Llama 3 70B: P=70.6B, layers=80, .
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
How many GPUs do I need to serve Llama 3 70B at FP16?+
FP16 weights are about 141 GB, so you need at least 2× 80 GB (A100/H100) GPUs with tensor parallelism just for weights, plus KV-cache per concurrent request. Most production deployments use 4× 80 GB for comfortable batch sizes.
Does Llama 3 70B fit on a single A100 80 GB?+
Only when quantized. At 4-bit (GPTQ/AWQ) the weights drop to roughly 35–40 GB, which fits with room for an 8K context KV cache. At FP16 or even INT8 (~71 GB) the margin is too small for real workloads.
How accurate is this Llama 3 70B VRAM estimate?+
It uses the exact architecture from the model's config.json (layers, heads, KV heads, head dimension) and standard serving math, so weight and cache figures are typically within a few percent. Real usage varies with your inference engine's allocator, paged-attention block size and activation buffers.
Does quantizing the KV cache hurt quality?+
INT8/FP8 KV cache is widely used in production (vLLM, TensorRT-LLM) with negligible quality loss on most tasks, and it halves cache memory. It matters most for long contexts, where the cache rivals or exceeds the weight memory.
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