Llama 3.1 8B Instruct

Deploy Llama 3.1 8B Instruct on a dedicated GPU from $0.20 per hour, billed per minute, on 1× RTX A4000. Meta's compact 8B Llama 3.1 instruction model, pairing 128K context and grouped-query attention with multilingual chat, code generation and tool use.

Text8B paramsLlama 3.1 Community LicenseDeploys in ~5 minSecure Cloud

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

HexGrid Cloud price

$0.20/hr

1× RTX A4000

Context

128K

tokens

Parameters

dense

Languages

officially supported

Running Llama 3.1 8B Instruct on HexGrid Cloud

What you get when you deploy with us, beyond the hourly rate.

Single tenant by default

The model runs on a GPU that is yours for the life of the instance. No shared endpoint, no queue behind other tenants.

Per-minute billing

You pay for the GPU, not the token. Stop the instance and billing stops with it.

OpenAI-compatible endpoint

Point an existing SDK at your instance by changing the base URL. No rewrite required.

Your weights, your data

Prompts and outputs stay inside your instance. Nothing is logged, sampled or used for training.

Llama 3.1 8B Instruct GPU sizing and cost

What the model needs at each precision, and the cheapest HexGrid Cloud configuration that holds it.

Llama 3.1 8B Instruct vRAM requirement and hourly cost by precision
Precision	vRAM needed	Runs on	Price
BF16 Recommended	20 GB	1× RTX 4090	$0.45/hr
FP8	10 GB	1× RTX A4000	$0.20/hr
INT4	5 GB	1× RTX A4000	$0.20/hr

Actual generation throughput depends heavily on GPU generation, quantization, batch size and prompt length. Benchmark on the HexGrid production configuration.

About Llama 3.1 8B Instruct

What Llama 3.1 8B Instruct is built for, and where it falls short.

Llama 3.1 8B Instruct is Meta's compact instruction-tuned Llama 3.1 checkpoint. It is a text-only autoregressive Transformer with grouped-query attention and a 128K context window.

At 8B parameters it is much easier to self-host than the 70B Llama variants while retaining the same 128K context class and Meta's supported chat-template/tool ecosystem.

Meta officially supports English, German, French, Italian, Portuguese, Hindi, Spanish and Thai.

What people run it for

Private chat

Self-host conversational assistants where 70B deployment is unnecessary or too expensive.

Lightweight coding assistance

Code explanation, generation and routine programming help.

Tool-driven assistants

Applications that combine a compact language model with external APIs and functions.

Long-document generation

Use the 128K context window for document-heavy workflows without moving to a much larger model.

Strengths

Compact enough for significantly cheaper deployment than 70B-class Llama models
128K context despite the 8B parameter count
Grouped-query attention improves inference scalability
Strong ecosystem support across Transformers, vLLM, SGLang and quantized runtimes
Tool-use capabilities and structured chat templates

Limitations

Substantially weaker reasoning and coding scores than larger contemporary 20B-70B models
Text only; no native image, audio or video input
Knowledge cutoff is December 2023
Custom Llama 3.1 Community License rather than Apache 2.0
Meta officially validates only eight spoken languages on the model card

Quickstart

Serve with vLLM and call the OpenAI-compatible chat-completions endpoint.

pythonPOST /v1/chat/completions

from openai import OpenAI

client = OpenAI(
    base_url="https://<your-instance>.hexgrid.cloud/v1",
    api_key="<your-key>",
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[
        {"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}
    ],
)

print(response.choices[0].message.content)

Meta's current Hugging Face page explicitly documents vLLM and SGLang OpenAI-compatible /v1/chat/completions serving.

Llama 3.1 8B Instruct reported benchmarks

Scores published by the model authors. A starting point, not a guarantee on your own data.

MMLU69.4
MMLU CoT73
MMLU-Pro CoT48.3
IFEval80.4
HumanEval pass@172.6
MATH CoT51.9
BFCL76.1

Llama 3.1 8B Instruct specifications

Architecture and serving details for Llama 3.1 8B Instruct.

PublisherMetaParameters8BLicenceLlama 3.1 Community LicenseReleasedJuly 2024

Architecture

Type: Dense autoregressive Transformer
Parameters: 8B
Attention: Grouped-query attention (GQA)
Post-training: Instruction tuned

Context

Context window: 128K tokens
Input: Multilingual text
Output: Text and code
Pretraining tokens: 15T+
Knowledge cutoff: December 2023

Serving

Engines: Transformers, vLLM, SGLang
API: OpenAI-compatible through common serving engines
Tool use: Supported

Llama 3.1 8B Instruct frequently asked questions

Common questions about deploying Llama 3.1 8B Instruct on HexGrid Cloud.

How large is the Llama 3.1 8B context window?

128K tokens.

Does Llama 3.1 8B support images?

No. This specific Instruct checkpoint is text-only.

Is it Apache 2.0?

No. It uses Meta's Llama 3.1 Community License.

What languages are officially supported?

English, German, French, Italian, Portuguese, Hindi, Spanish and Thai.

Deploy Llama 3.1 8B Instruct today

$0.20 per hour on 1× RTX A4000, billed per minute, never shared.

Deploy Llama 3.1 8B Instruct See all models