Llama 3.1 405B VRAM Calculator
Estimate GPU memory to run Llama 3.1 405B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Llama 3.1 405B is the largest open-weights dense model; even at FP8 the weights need ~406 GB, so it is a multi-node deployment (e.g. 8× H100 80 GB with FP8).
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 3.1 405B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Llama 3.1 405B 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 3.1 405B is the largest open-weights dense model; even at FP8 the weights need ~406 GB, so it is a multi-node deployment (e.g. 8× H100 80 GB with FP8). 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 3.1 405B VRAM Calculator
- 1Enter your values into Llama 3.1 405B 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 3.1 405B VRAM Calculator?
- ✓Computes Llama 3.1 405B 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 3.1 405B: P=405.85B, layers.
- ✓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
What hardware does Llama 3.1 405B actually need?+
Meta's reference serving setup is a full 8× H100 80 GB node (640 GB) running FP8 weights (~406 GB) with the remainder for KV cache. At BF16 you need two such nodes with pipeline or tensor parallelism across 16 GPUs.
How big is the 405B KV cache at the full 128K context?+
With GQA (8 KV heads, head_dim 128) one 128K-token sequence costs about 2 × 126 × 8 × 128 × 131072 × 2 bytes ≈ 63 GB at FP16 — nearly a full H100 per request, which is why long-context serving uses FP8 KV and paged attention.
How accurate is this Llama 3.1 405B 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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