Reviewed repository / hardware evidence

Phi-3.5-mini-instruct: GPU & VRAM requirements

Phi-3.5-mini-instruct is a 3.82 billion-parameter Microsoft repository with a Phi3ForCausalLM architecture, MIT license metadata, and a multilingual model-card label. Its size places it below the generic 7B tier, so an 8 GB card can support more than one precision floor before cache and runtime costs are added.

See the precision plan → Inspect the source repository →

Decision summary

Where the memory boundary actually lands.

The shared calculation produces 2.14 GiB at INT4, 4.27 GiB at INT8, and 8.54 GiB at BF16 after 20% headroom. INT4 and INT8 clear the 7.2 GiB usable budget on an 8 GB card. BF16 does not; it first clears a conservative 12 GB tier. Exact checkpoint bytes can replace these parameter-derived estimates in the calculator.

Why this page exists. The page is independently useful because it distinguishes the 3.82B repository from generic 7B advice and shows the precise point where BF16 crosses from 8 GB to 12 GB.

Weights first / precision by precision

The reproducible memory floor.

Every row uses 3.82B parameters × bits ÷ 8 ÷ 1024³, then adds 20% planning headroom. The single-card tier assumes 90% of labeled VRAM is available.

Mode	Bits	Raw weights	+ 20% floor	First single-card tier	24 GB GPU count*	Calculator
INT4	4 bits	1.78 GiB	2.14 GiB	8 GB	1	Edit plan →
INT8	8 bits	3.56 GiB	4.27 GiB	8 GB	1	Edit plan →
BF16	16 bits	7.12 GiB	8.54 GiB	12 GB	1	Edit plan →

*GPU count is capacity division against 90% usable VRAM. It does not prove the runtime can shard the model, avoid replication, or deliver acceptable interconnect performance.

INT4 card-fit matrix

Test the floor against common memory tiers.

Positive margin means the INT4 weight-plus-headroom floor fits inside the declared usable budget. It is not leftover context capacity: cache, runtime workspace, and system allocations are still unknown.

Card memory	90% usable	One-card result	Capacity-only count
8 GB	7.2 GiB	Clears by 5.06 GiB	1
12 GB	10.8 GiB	Clears by 8.66 GiB	1
16 GB	14.4 GiB	Clears by 12.26 GiB	1
24 GB	21.6 GiB	Clears by 19.46 GiB	1
32 GB	28.8 GiB	Clears by 26.66 GiB	1
48 GB	43.2 GiB	Clears by 41.06 GiB	1
80 GB	72 GiB	Clears by 69.86 GiB	1
96 GB	86.4 GiB	Clears by 84.26 GiB	1
141 GB	126.9 GiB	Clears by 124.8 GiB	1

Public source record

What the repository declares.

Metadata refreshed Aug 15, 2026. Open the source before relying on any field.

Open microsoft/Phi-3.5-mini-instruct →

Repository: microsoft/Phi-3.5-mini-instruct
Safetensors total: 3.82B parameters
Architecture: Phi3ForCausalLM
Model type: phi3
Library: transformers
License metadata: mit
Access gated: No
Base model: Not declared in card metadata
Languages: multilingual
Created: Aug 16, 2024
Last modified: Dec 10, 2025
Hub signal: 984.9K downloads · 1.1K likes

Do not confuse capacity with deployment

Repository-specific caution.
A multilingual label and high download count do not establish quality for a particular language or task. The formula also says nothing about context length, KV-cache growth, CPU or unified-memory offload, or tokens per second. Confirm the model card, runtime support, and the exact artifact before treating the capacity tier as a deployment plan.

Formula boundary.
No KV cache, activations, graph capture, allocator fragmentation, offload, operating-system reserve, throughput, latency, or power is included.

Source boundary.
Hub trend, downloads, and likes help discover repositories. They are not a benchmark, endorsement, security review, or license opinion.

Before downloading weights

Three checks specific to this repository.

Confirm Phi3ForCausalLM support and the exact tokenizer or chat template in the runtime selected for the experiment.
Use language-specific evaluations before relying on the multilingual card label for any customer-facing workflow.
If BF16 is required, start at 12 GB under this policy and then add cache, context, and runtime measurements before deployment.

Passive backlink / model-card distribution

Let the repository carry its hardware evidence.

Add this passive SVG badge to a README, model card, documentation page, or benchmark report. It states the source-backed Phi-3.5-mini-instruct INT4 floor and links to the assumptions—not to a download or quality claim.

Why this repository benefits. This badge helps Phi documentation avoid generic 7B advice: the exact 3.82B total keeps INT4 and INT8 inside an 8 GB policy, while the linked precision ladder shows why BF16 first clears the reviewed 12 GB tier.

Markdown for a README or model card

[![Phi-3.5-mini-instruct INT4 floor 2.14 GiB - ResearchAudio](https://tools.researchaudio.io/badges/models/phi-3-5-mini-instruct-gpu-requirements.svg)](https://tools.researchaudio.io/models/phi-3-5-mini-instruct-gpu-requirements/?utm_source=model_badge&utm_medium=model_card&utm_campaign=ai_evidence_lab&utm_content=phi-3-5-mini-instruct-gpu-requirements#hardware-plan)

Badge visits use utm_source=model_badge, utm_medium=model_card, and this repository’s slug. Copying does not submit or store anything.

Nearest reviewed parameter totals

These pages use the same formula and source rules, making the capacity boundary comparable without pretending the models have equivalent quality or runtime behavior.

Phi-3.5-mini-instruct FAQ

Three answers with the assumptions attached.

Use these as a first-pass hardware screen. Replace parameter arithmetic with exact artifact bytes and architecture-aware cache inputs before deployment.

Open the editable GPU calculator →

How much VRAM does Phi-3.5-mini-instruct need?

Phi-3.5-mini-instruct has 3.82 billion safetensors parameters in the public Hub metadata. The weight-plus-20%-headroom floors are 2.14 GiB at INT4, 4.27 GiB at INT8, and 8.54 GiB at BF16. These estimates exclude KV cache, activations, runtime workspace, fragmentation, offload, and speed.

Can Phi-3.5-mini-instruct run on one GPU?

The first listed card tier whose 90%-usable budget clears the INT4 weight floor is 8 GB. That is an arithmetic starting point, not a runtime guarantee.

Does the Hugging Face signal prove Phi-3.5-mini-instruct is good?

No. Downloads, likes, and trending position are discovery signals. They do not prove task quality, safety, speed, runtime compatibility, or licensing suitability.