Llama 3 8B VRAM Calculator
Estimate GPU memory to run Llama 3 8B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Meta's Llama 3 8B uses grouped-query attention (8 KV heads vs 32 query heads), which cuts KV-cache memory 4× compared with Llama 2 7B at the same context length.
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 8B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Llama 3 8B 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. Meta's Llama 3 8B uses grouped-query attention (8 KV heads vs 32 query heads), which cuts KV-cache memory 4× compared with Llama 2 7B at the same context length. 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 8B VRAM Calculator
- 1Enter your values into Llama 3 8B 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 8B VRAM Calculator?
- ✓Computes Llama 3 8B 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 8B: P=8.03B, layers=32, k.
- ✓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
Can Llama 3 8B run on a 12 GB GPU?+
Yes, in 4-bit quantization. The INT4 weights need roughly 4.5 GB, leaving headroom for the KV cache and CUDA overhead on a 12 GB card such as an RTX 3060. FP16 inference needs about 16 GB of weights alone, so it will not fit unquantized.
Why does Llama 3 8B need less KV-cache memory than Llama 2 7B?+
Llama 3 uses grouped-query attention with only 8 key-value heads, while Llama 2 7B caches all 32 heads. Per token per layer that is 4× fewer cached vectors, so an 8K-token context costs about 1.1 GB instead of over 4 GB at FP16.
How accurate is this Llama 3 8B 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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