Phi-3 Mini 3.8B VRAM Calculator
Estimate GPU memory to run Phi-3 Mini 3.8B — weights, KV cache and overhead at FP16/INT8/INT4, with a fits-on-which-GPU verdict.
Microsoft's Phi-3 Mini targets on-device use: 4-bit weights are ~2.2 GB, small enough for phones and 4 GB GPUs, though its full-MHA cache grows quickly with context.
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 Phi-3 Mini 3.8B VRAM Calculator
This calculator estimates how much GPU memory (VRAM) you need to run Phi-3 Mini 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. Microsoft's Phi-3 Mini targets on-device use: 4-bit weights are ~2.2 GB, small enough for phones and 4 GB GPUs, though its full-MHA cache grows quickly with context. 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 Phi-3 Mini 3.8B VRAM Calculator
- 1Enter your values into Phi-3 Mini 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 Phi-3 Mini 3.8B VRAM Calculator?
- ✓Computes Phi-3 Mini 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)) — Phi-3 Mini 3.8B: P=3.82B, 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
Can Phi-3 Mini really run on a phone?+
Yes — the 4-bit ONNX/GGUF builds are about 2.2–2.4 GB and run on recent flagship NPUs/GPUs at usable speeds. Keep contexts short: with 32 full KV heads, cache memory grows ~6× faster per token than a GQA model of similar size.
Why does Phi-3 Mini use full multi-head attention instead of GQA?+
At 3.8B parameters the absolute cache cost is small for short contexts, and full MHA preserves quality for the dense-reasoning benchmarks Phi targets. The 128K-context variant switches to a different attention scheme to stay practical.
How accurate is this Phi-3 Mini 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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