Reviewed repository / hardware evidence

Llama-3.1-8B-Instruct: GPU & VRAM requirements

Llama-3.1-8B-Instruct remains a widely downloaded reference point, with 8.03 billion safetensors parameters in the Hub metadata. Its repository uses the LlamaForCausalLM architecture and is access-gated under the Llama 3.1 license. The useful hardware split is clear: 8 GB is an INT4 starting point, while higher precision moves the single-card boundary.

See the precision plan → Inspect the source repository →

Decision summary

Where the memory boundary actually lands.

The weight-plus-headroom floor is 4.49 GiB at INT4, 8.97 GiB at INT8, and 17.95 GiB at BF16. Under the same 90%-usable rule, INT4 clears an 8 GB card, INT8 first clears 12 GB, and BF16 first clears 24 GB. Those thresholds leave different amounts for cache, runtime workspace, and the operating system.

Why this page exists. This page provides a source-specific precision ladder for the exact instruct repository and adds the gated-license boundary that generic 8B sizing pages often omit.

Weights first / precision by precision

The reproducible memory floor.

Every row uses 8.03B 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	3.74 GiB	4.49 GiB	8 GB	1	Edit plan →
INT8	8 bits	7.48 GiB	8.97 GiB	12 GB	1	Edit plan →
BF16	16 bits	14.96 GiB	17.95 GiB	24 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 2.71 GiB	1
12 GB	10.8 GiB	Clears by 6.31 GiB	1
16 GB	14.4 GiB	Clears by 9.91 GiB	1
24 GB	21.6 GiB	Clears by 17.11 GiB	1
32 GB	28.8 GiB	Clears by 24.31 GiB	1
48 GB	43.2 GiB	Clears by 38.71 GiB	1
80 GB	72 GiB	Clears by 67.51 GiB	1
96 GB	86.4 GiB	Clears by 81.91 GiB	1
141 GB	126.9 GiB	Clears by 122.4 GiB	1

Public source record

What the repository declares.

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

Open meta-llama/Llama-3.1-8B-Instruct →

Repository: meta-llama/Llama-3.1-8B-Instruct
Safetensors total: 8.03B parameters
Architecture: LlamaForCausalLM
Model type: llama
Library: transformers
License metadata: llama3.1
Access gated: Yes (manual)
Base model: meta-llama/Meta-Llama-3.1-8B
Languages: en, de, fr, it, pt, hi, es, th
Created: Jul 18, 2024
Last modified: Sep 25, 2024
Hub signal: 7.6M downloads · 6.6K likes

Do not confuse capacity with deployment

Repository-specific caution.
Access approval and license acceptance are separate from hardware fit. The page does not assume a context length, quantization implementation, throughput target, or runtime. The popular model name also appears on many converted repositories; verify that the artifact you select corresponds to the source and precision used in your calculation.

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.

Obtain access through the gated repository and review the Llama 3.1 license before planning distribution or production use.
Match the calculator to the exact official or converted checkpoint rather than transferring the 8.03B parameter floor blindly.
Add the intended context and batch behavior before deciding whether INT8 belongs on 12 GB or should move to a roomier tier.

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 Llama-3.1-8B-Instruct INT4 floor and links to the assumptions—not to a download or quality claim.

Why this repository benefits. This badge is appropriate for the exact gated instruct repository, not every Llama conversion. It carries the 4.49 GiB INT4 floor into documentation while the linked evidence page keeps access approval, Llama 3.1 licensing, and artifact identity visible.

Markdown for a README or model card

[![Llama-3.1-8B-Instruct INT4 floor 4.49 GiB - ResearchAudio](https://tools.researchaudio.io/badges/models/llama-3-1-8b-instruct-gpu-requirements.svg)](https://tools.researchaudio.io/models/llama-3-1-8b-instruct-gpu-requirements/?utm_source=model_badge&utm_medium=model_card&utm_campaign=ai_evidence_lab&utm_content=llama-3-1-8b-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.

Llama-3.1-8B-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 Llama-3.1-8B-Instruct need?

Llama-3.1-8B-Instruct has 8.03 billion safetensors parameters in the public Hub metadata. The weight-plus-20%-headroom floors are 4.49 GiB at INT4, 8.97 GiB at INT8, and 17.95 GiB at BF16. These estimates exclude KV cache, activations, runtime workspace, fragmentation, offload, and speed.

Can Llama-3.1-8B-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 Llama-3.1-8B-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.