Name: Llama 4
Brand: Meta
Availability: InStock

Question 1

What is Llama 4?

Accepted Answer

Llama 4 is a foundational family of auto-regressive large language models released by Meta that natively support multimodal inputs (text and images) using early fusion. The series introduces two efficient 17B-parameter mixture-of-experts (MoE) variants—Scout (16 experts) and Maverick (128 experts)—designed to deliver industry-leading performance in text generation, visual reasoning, captioning, and image understanding while reducing inference compute via sparse expert routing. Instruction-tuned variants enable assistant-style chat and visual QA, and the models are designed for commercial and research use under the Llama 4 Community License. Llama 4 emphasizes steerability through system prompts, scalable multi-GPU inference, and availability via repositories and cloud marketplaces for integration and fine-tuning.

Question 2

How much does Llama 4 cost?

Accepted Answer

Llama 4 is a paid service with various pricing tiers.

Question 3

Who developed Llama 4?

Accepted Answer

Llama 4 was developed by Meta. Meta AI research division developing foundation models and tools

Question 4

What are the key features of Llama 4?

Accepted Answer

Llama 4 offers the following key features: Mixture-of-Experts Architecture: Uses MoE routing to scale effective model capacity while keeping per-token compute lower, enabling large parameter counts (sparse experts) without proportional inference cost., Two Efficient 17B Models: Offers Llama 4 Scout (17B, 16 experts) and Llama 4 Maverick (17B, 128 experts) targeting different trade-offs of throughput, latency, and capability., Native Multimodality with Early Fusion: Accepts text and image inputs with an early-fusion design for integrated image-text understanding, captioning, and visual question answering., Instruction-Tuned Variants: Provides Instruct/Assistant-tuned checkpoints optimized for conversational behavior, visual reasoning, and producing helpful, formatted responses., Steerability via System Prompts: Enables developers to shape tone, refusal behavior, and response style through system prompts to reduce hallucinations and improve conversationality., Open Distribution & Community License: Distributed under the Llama 4 Community License with attribution and usage terms, enabling commercial and research use subject to license conditions and geographic restrictions., Scalable Multi-GPU Inference: Supported by reference code and community tooling that document multi-GPU setups (examples require 4–8 high-end GPUs like H100 for full precision inference) and integration with cloud marketplaces and repositories., Natively multimodal (text + images) with early fusion for native multimodality, Mixture-of-Experts (MoE) architecture (e.g., Scout: 17B w/16 experts; Maverick: 17B w/128 experts), Instruction-tuned variants optimized for assistant-like chat and visual reasoning, Supports image-text-to-text tasks: visual recognition, captioning, image Q&A, and visual reasoning, Distributed model weights, training/inference code, and fine-tuning tooling provided, Precision support and optimization options (bf16 inference; FP8 variants available), Can be run locally with multi-GPU setups and community scripts (torchrun examples provided), Available via community/hosting platforms (Hugging Face model pages, GitHub repos) and compatible with Llama Stack and other ecosystem tooling, License-controlled distribution (Llama 4 Community License) with access request and attribution requirements.

Llama 4

Llama 4

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