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Run it on your own hardware · updated 2026-10-16

Phi-4-mini-instruct
Memory at each precision, the licence in plain words, and where it fits.

Short answer. Phi-4-mini-instruct needs about 4.7 GB of memory at 4-bit, 6.6 GB at 8-bit and 10 GB at full 16-bit precision, including runtime overhead and an 8,192-token KV cache. It has about 3.8 billion parameters and a 131,072-token context window. Licence in short: commercial use allowed. By memory alone, on ANVE’s launch configurations: ANVE One fits at BF16, ANVE Office fits at BF16, ANVE Office Pro (GPU) fits at BF16. These are estimates from its published size, not benchmarks.

01

What it is

Phi-4-mini-instruct is a language model for chat, drafting and question answering, published by Microsoft on Hugging Face on 19 February 2025. It has about 3.8 billion parameters and a configured context window of 131,072 tokens.

Its card lists the licence as MIT. In short, the licence allows commercial use, modification and redistribution as long as the copyright notice is kept.

Hugging Face counted about 358,028 downloads of it in the 30 days before we checked (7 Oct 2026), and 847 people have liked it there.

The card lists 24 languages: multilingual, ar, zh, cs, da, nl, en, fi, fr, de, he, hu and 12 more (ISO codes as given on the card).

02

How much memory it needs

How these figures are worked out: weights = parameters × bits per weight ÷ 8. The KV cache is added for 8,192 tokens of context, plus about 1.5 GB of runtime overhead. Treat the totals as planning estimates.

Each token of context adds about 0.13 MB of KV cache at 16-bit, so 32,768 tokens would add about 4.3 GB.

PrecisionWeightsKV cache (8,192 tokens)Total, estimated
BF16 (full precision)7.7 GB1.1 GB10 GB
8-bit (Q8_0)4.1 GB1.1 GB6.6 GB
4-bit (Q4_K)2.2 GB1.1 GB4.7 GB
03

Architecture at a glance

SettingValue in config.json
Architecture classPhi3ForCausalLM
Model typephi3
Layers32
Hidden size3,072
Attention heads24
Key-value heads8 (grouped-query attention)
Head size128
Vocabulary200,064
Configured context131,072
Stored precisionbfloat16
04

Will it fit on ANVE?

These verdicts are arithmetic, not tests, against the specification at launch: ANVE One can give a model about three quarters of its 64 GB of unified memory, while ANVE Office and ANVE Office Pro hold it on a 24 GB graphics card and keep the rest of the stack in 64 GB or 128 GB of system memory. ANVE’s tested default is Qwen 3.8 27B; this model is not yet validated on ANVE.

Configuration (at launch)Memory for the modelVerdict
ANVE Oneabout 48 GB of 64 GB unified memoryfits at BF16
ANVE Office24 GB on the RTX PRO 4000 Blackwell SFF Editionfits at BF16
ANVE Office Pro (GPU)24 GB on the RTX PRO 4000 Blackwellfits at BF16
05

What an office would use it for

This size suits quick drafting, classification, extraction and chat over short documents on modest hardware. ANVE pairs a fast 4B-class model with its main model for quick jobs.

06

Similar models to compare

  • Nemotron-3-Nano-4B (4.0B parameters, about 3.7 GB at 4-bit before context; custom licence: check before commercial use)
  • granite-4.2-3b (3.7B parameters, about 3.6 GB at 4-bit before context; commercial use allowed)
  • Qwen3-4B (4.0B parameters, about 3.8 GB at 4-bit before context; commercial use allowed)
07

Questions people ask

How much memory does Phi-4-mini-instruct need?
About 4.7 GB at 4-bit, 6.6 GB at 8-bit and 10 GB at full 16-bit precision, including an 8,192-token KV cache and about 1.5 GB of runtime overhead. These are estimates from its parameter count.
Can Phi-4-mini-instruct run on a 24 GB graphics card?
Yes, at BF16 by this estimate, with room left for context.
Is Phi-4-mini-instruct free for commercial use?
Its card lists MIT, which allows commercial use, modification and redistribution as long as the copyright notice is kept. Read the licence itself before relying on this summary; it is not legal advice.
What is Phi-4-mini-instruct’s context window?
Its configuration sets 131,072 tokens. Longer contexts need more memory for the KV cache, so the practical limit on a given machine can be lower.
08

What is not real yet

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.