Exploring Llama 4: The Latest in AI for Business

On April 5, 2025, Meta released Llama 4—a new generation of open-weight, multimodal foundation models. As the first openly available models in the Llama 4 “herd,” Llama 4 Scout and Llama 4 Maverick represent a significant leap in AI capability and enterprise usability. Both models are built with a Mixture-of-Experts (MoE) architecture and offer native multimodal processing across text, images, and video. They support record-breaking context lengths and outperform several commercial closed models on core AI benchmarks.

Meta’s Llama 4 family was developed with transparency, performance, and flexibility in mind. The models are designed to be fine-tuned, deployed privately, and integrated into real-world workflows across research labs and enterprises—without reliance on proprietary APIs.

1. Mixture-of-Experts (MoE) efficiency

Llama 4 is the first Llama series to adopt MoE layers. These architectures activate only a subset of parameters per token—boosting training and inference efficiency. Both Scout and Maverick share a 17B active parameter core, but differ in expert count and total parameter size:

• Llama 4 Scout uses 16 experts and has 109B total parameters. It is optimized for efficiency and fits on a single NVIDIA H100 GPU using Int4 quantization.
• Llama 4 Maverick includes 128 experts and has 400B total parameters, providing best-in-class general-purpose and assistant performance while still being deployable on a single H100 host.

These designs enable Llama 4 to offer higher performance-per-dollar than similarly sized dense models.

2. Unprecedented context length

Llama 4 Scout supports up to 10 million tokens of context—the highest available among open or proprietary models. This enables new applications in:

• Multi-document reasoning
• Long-form retrieval-augmented generation (RAG)
• Temporal video summarization
• Codebase and contract analysis

Maverick, while optimized for assistant use cases, offers a substantial 1M-token context window, ensuring deep memory retention in chats and iterative problem-solving.

3. Multimodal and multilingual by design

Both models are natively multimodal. Trained with early fusion methods, they integrate text and image (and video frame) data within a unified model backbone. This enables real-time reasoning over text and visuals—ideal for tasks like:

• Visual Q&A
• Image-based recommendations
• Graph/chart interpretation
• Multi-image summarization (up to 8 images tested successfully)

They also support 200 languages, with 12+ fully supported out-of-the-box. This gives global enterprises the ability to deploy a single AI instance across multiple regions and language markets.

4. Model performance benchmarks

• Llama 4 Scout outperforms Google’s Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 in its class across reasoning, coding, and image benchmarks.
• Llama 4 Maverick beats GPT-4o and Gemini 2.0 Flash in multimodal and long-context tasks and competes with DeepSeek v3 on coding—despite using less than half the active parameters.

Performance gains stem from distillation from Llama 4 Behemoth (288B active, ~2T total), Meta’s unreleased flagship model that outperforms GPT-4.5 and Claude Sonnet 3.7 on STEM benchmarks like GPQA and MATH-500.

1. Scalable knowledge processing
   With 10M-token capacity, Scout unlocks massive knowledge management workflows. Enterprises can now feed full regulatory filings, legal contracts, or scientific archives into a single session and extract insights without chunking or fine-tuning.
2. Reliable, regulation-ready intelligence
   Meta’s enhanced safety techniques—including online reinforcement learning and direct preference optimization—make Llama 4 significantly more balanced and less prone to hallucinations. For BFSI, legal, or healthcare organizations, this improves compliance, accuracy, and auditability.
3. Custom AI with full control
   As an open-weight model, Llama 4 allows full customization. Enterprises can:
   a. Fine-tune on proprietary datasets
   b. Control deployment (cloud or on-prem)
   c. Audit responses
   d. Build domain-specific assistants
   This flexibility stands in contrast to closed models that limit customization or require sending sensitive data to external APIs.
4. Cross-functional automation
   Llama 4 supports:
   a. Developer acceleration (IDE integration, debugging, code summarization)
   b. Customer service (multilingual chat, visual support, ticket summarization)
   c. Enterprise content creation (drafting reports, analyzing documents, transforming visuals into text insights)
5. Visual reasoning and personalization
   With enhanced visual grounding and image alignment capabilities, Llama 4 can localize, describe, and infer context from visuals—useful in retail, logistics, manufacturing, and healthcare workflows.

• Hardware: Maverick and Scout require H100-class GPUs; Scout can run on a single 80GB card, but long context or image tasks are compute-intensive.
• Fine-tuning complexity: MoE models are more complex to customize. Parameter-efficient tuning like LoRA or prompt engineering is advised for early deployments.
• No vendor SLA: Enterprises must handle updates, safety layers, and integration without managed support—though this also means greater independence and version control.

As open foundation models continue to close the gap with closed alternatives, Llama 4 sets the benchmark for enterprise-grade, open AI systems. With unprecedented transparency, scale, and extensibility, it redefines what's possible with open-weight architectures—but successful integration requires expert talent and scalable infrastructure. 

That’s where Turing comes in. As one of the world’s fastest-growing AGI infrastructure companies, Turing works with the leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge. 

What to explore what's possible with Llama 4 or other foundation models? Let's talk.