Meta: Llama 3.2 3B Instruct

Model Overview

Full Name: Meta: Llama 3.2 3B Instruct Model ID: meta-llama/llama-3.2-3b-instruct Primary Provider: DeepInfra Created: September 25, 2024 Model Type: Language Model - Instruction-tuned Parameters: 3 billion

Description

Llama 3.2 3B is a 3-billion-parameter multilingual large language model optimized for advanced natural language processing tasks including dialogue generation, complex reasoning, and text summarization. Trained on 9 trillion tokens using the latest transformer architecture, this model excels in instruction-following and complex reasoning tasks. Supporting eight core languages natively, it offers a sweet spot between computational efficiency and capability, making it ideal for production deployments with moderate compute resources.

Technical Specifications

Context & Output Limits

Maximum Context Window: 131,072 tokens (up to 131.1K per provider)
Maximum Output: 16,384-131,072 tokens (varies by provider)
Extended Context: Some providers offer up to 128K context

Training & Architecture

Training Data: 9 trillion tokens
Training Date: September 25, 2024
Quantization: Available in bf16 and fp8 variants
Quantization Variants: bfloat16 (bf16) primary, fp8 available from some providers
Languages Supported: 8 core languages
- English
- German
- French
- Italian
- Portuguese
- Hindi
- Spanish
- Thai

Model Weights & Resources

Model Weights: Available on Hugging Face
Model Card: GitHub

Pricing

Provider Pricing Comparison

Provider	Input Price	Output Price	Context	Max Output
Context Window	128,000 tokens
DeepInfra	$0.02/M	$0.02/M	131.1K	16.4K
NovitaAI	$0.024/M	$0.04/M	32.8K	32K
Cloudflare	$0.051/M	$0.34/M	128K	128K
Together	$0.06/M	$0.06/M	131.1K	16.4K
Hyperbolic	$0.10/M	$0.10/M	131.1K	131.1K

Recommended: DeepInfra offers the best pricing for this model at $0.02 per 1M tokens for both input and output.

Supported Parameters

The following parameters are supported for inference requests:

max_tokens - Maximum tokens to generate
temperature - Sampling temperature (0.0-2.0)
top_p - Nucleus sampling parameter
top_k - Top-k sampling parameter
stop - Stop sequences for generation
frequency_penalty - Adjust token frequency penalties
presence_penalty - Penalize token presence
repetition_penalty - Penalize repetitive content
seed - Random seed for reproducibility
min_p - Minimum probability parameter
response_format - JSON mode and structured outputs

Use Cases

This model is particularly well-suited for:

Production Inference: Cost-effective LLM deployment with good performance balance
Multilingual Applications: Native support for 8 languages in a single model
Reasoning Tasks: Complex reasoning with moderate computational requirements
Customer Support: Automated support agents with natural dialogue
Content Generation: Articles, summaries, and structured writing
Code Analysis: Understanding and explaining code (not primary code generation)
Question Answering: RAG systems and knowledge retrieval
Dialogue Systems: Conversational AI with strong language understanding
Data Extraction: Structured information extraction from text
Enterprise Automation: Business process automation with language understanding

Provider Details

DeepInfra (Recommended - Best Price)

Pricing: $0.02/M input, $0.02/M output Uptime: 99.9% (24h) Performance:

Average Latency: 0.35 seconds
Throughput: 68.26 tokens/second
Quantization: bf16

Data Policy:

Prompt Training: False
Prompt Logging: Zero retention
Moderation: Responsibility of developer

Features: Standard OpenAI-compatible API

NovitaAI (Balanced Option)

Pricing: $0.024/M input, $0.04/M output Context: 32.8K tokens Performance:

Average Latency: 0.71 seconds
Throughput: 162.3 tokens/second
Quantization: bf16

Cloudflare (Extended Context)

Pricing: $0.051/M input, $0.34/M output Context: 128K tokens Performance:

Average Latency: 0.31 seconds
Throughput: 206.4 tokens/second
Quantization: bf16

Together (Ultra Fast)

Pricing: $0.06/M input, $0.06/M output Performance:

Average Latency: 0.75 seconds
Throughput: 111.5 tokens/second
Quantization: fp8

Hyperbolic (Full Context)

Pricing: $0.10/M input, $0.10/M output Context: 131.1K tokens Max Output: 131.1K tokens Performance:

Average Latency: 1.04 seconds
Throughput: 103.3 tokens/second
Quantization: fp8

Performance Statistics

Real-time Metrics (Across Providers)

Metric	Best	Average	Slowest
Latency	0.31s (CF)	0.63s	1.04s (HB)
Throughput	206.4 tps (CF)	150.3 tps	68.26 tps (DI)
Uptime	99.9%+	99.8%	99%+

Usage Patterns

The model sees strong adoption for:

Production Deployments: Cost-effective production inference
Reasoning Tasks: Complex multi-step problem solving
Multilingual Chat: Native support for 8 languages
Summarization: Document and text summarization
Code Understanding: Code analysis and explanation (not generation-optimized)

Smaller Models

Llama 3.2 1B Instruct - Lightweight 1B parameter version for edge deployment

Larger Models

Llama 3.3 8B Instruct - Lightweight variant of Llama 3.3 70B for quick responses
Llama 3.3 70B Instruct - Full multilingual model with 8 language support
Llama 3.1 405B Instruct - Flagship 400B parameter model with 128k context
Llama 3.1 70B Instruct - Larger, more capable instruction-tuned variant

Multimodal Variants

Llama 3.2 90B Vision Instruct - Multimodal version with 90B parameters
Llama 3.2 11B Vision Instruct - Smaller multimodal variant with 11B parameters

Legacy Models

Llama 3.1 8B Instruct - Previous generation 8B instruction-tuned model
Llama 3 8B Instruct - Llama 3 family 8B variant
Llama 2 13B Chat - Earlier generation 13B chat model

API Integration

Example Request (cURL - DeepInfra)

curl https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-3.2-3b-instruct",
    "messages": [
      {"role": "user", "content": "Explain machine learning in simple terms"}
    ],
    "max_tokens": 200,
    "temperature": 0.7
  }'

LangMart SDK Example

import OpenAI from "openai"  // LangMart compatible;

const client = new OpenAI({
  apiKey: process.env.LANGMART_API_KEY,
});

const completion = await client.chat.completions.create({
  model: "meta-llama/llama-3.2-3b-instruct",
  messages: [
    {
      role: "user",
      content: "Translate the following to French: 'How are you today?'",
    },
  ],
  temperature: 0.3,
  max_tokens: 150,
});

console.log(completion.choices[0]?.message?.content);

Multi-language Example

// Supports 8 languages natively
const messages = [
  {
    role: "user",
    content: "Por favor, resume los puntos principales de este artículo: [article]"
  }
];

const response = await client.chat.completions.create({
  model: "meta-llama/llama-3.2-3b-instruct",
  messages,
  temperature: 0.5,
});

Links & Resources

Documentation: https://langmart.ai/model-docs.2-3b-instruct
Chat Interface: https://langmart.ai/chat
Compare Models: https://langmart.ai/model-docs
Hugging Face: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
Model Card: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md
Use Policy: https://www.llama.com/llama3/use-policy/

Key Advantages

Optimal Balance: 3B parameters provide excellent cost-to-capability ratio
Extended Context: Up to 131K token context window for long documents
Affordable: Starting at $0.02 per 1M tokens with DeepInfra
Multilingual: Native support for 8 languages without switching models
Fast Inference: Average 150+ tokens/second across providers
Production-Ready: High uptime and reliability across multiple providers
Instruction-Tuned: Excellent at following specific instructions
Multiple Providers: LangMart routes to 5+ providers for redundancy

Limitations & Considerations

Reasoning Depth: Limited for very complex multi-step reasoning
Knowledge Cutoff: Trained on data up to a certain date
Hallucinations: May generate plausible-sounding but false information
Code Generation: Not optimized for code generation (use Code Llama)
Factual Accuracy: Should be fact-checked for critical applications
Specialized Domains: May not have deep expertise in specialized fields

Performance Recommendations

When to Use This Model

Production systems with cost sensitivity
Real-time inference with moderate latency requirements
Multilingual applications
Moderate complexity tasks

When to Choose Alternatives

For Speed: Use 1B variant (Llama 3.2 1B Instruct)
For Complex Reasoning: Use 70B+ models
For Code Generation: Use Code Llama or larger models
For Vision: Use Llama 3.2 Vision models

Last Updated: December 24, 2024 Data Source: LangMart Status: Active & Available on Multiple Providers

Meta: Llama 3.2 3B Instruct

Meta: Llama 3.2 3B Instruct

Model Overview

Description

Technical Specifications

Context & Output Limits

Training & Architecture

Model Weights & Resources

Pricing

Provider Pricing Comparison

Supported Parameters

Use Cases

Provider Details

DeepInfra (Recommended - Best Price)

NovitaAI (Balanced Option)

Cloudflare (Extended Context)

Together (Ultra Fast)

Hyperbolic (Full Context)

Performance Statistics

Real-time Metrics (Across Providers)

Usage Patterns

Related Models from Meta Llama

Smaller Models

Larger Models

Multimodal Variants

Legacy Models

API Integration

Example Request (cURL - DeepInfra)

LangMart SDK Example

Multi-language Example

Links & Resources

Key Advantages

Limitations & Considerations

Performance Recommendations

When to Use This Model

When to Choose Alternatives