Llama 4 models substantially improve efficiency and capability, especially in handling multimodal input and extended context lengths. The hardware requirements differ depending on the model you're running, Scout, Maverick, or the upcoming Behemoth. Here's a breakdown of what to expect when planning for inference or training.

Llama 4 Scout

Scout is designed to be efficient while supporting an unprecedented 10 million token context window. Under certain conditions, it fits on a single NVIDIA H100 GPU with 17 billion active parameters and 109 billion total. This makes it a practical starting point for working with long-context or document-level tasks.

“Under certain conditions” refers to a narrow setup where Scout can fit on a single H100:

Quantized to INT4 or similar: FP16 versions exceed the VRAM of an 80GB H100. Compression is mandatory.
Short or moderate contexts: 4K to 16K contexts are feasible. Beyond that, the KV cache dominates memory usage.
Batch size of 1: Larger batches require more VRAM or GPUs.
Efficient inference frameworks: Tools like vLLM, AutoAWQ, or ggml help manage memory fragmentation and loading overhead.

So, fitting Scout on one H100 is possible, but only in highly constrained conditions.

Inference Requirements (INT4, FP16):

Context Length	INT4 VRAM	FP16 VRAM
4K Tokens	~99.5 GB / ~76.2 GB	~345 GB
128K Tokens	~334 GB	~579 GB
10M Tokens	Dominated by KV Cache, estimated ~18.8 TB	Same as INT4, due to KV dominance

Training Requirements (FP16):

4K Tokens: ~2.3 TB VRAM
Requires distributed setup, even for modest sequence lengths

Scout is effectively quantized and optimized for long-context tasks, including summarizing vast codebases, multi-document chains, or interacting with large user histories. It supports 256K context during training and shows strong generalization up to 10M tokens in inference. However, KV cache memory quickly becomes the bottleneck beyond 128K tokens, making extreme context lengths feasible only with aggressive engineering and memory strategies like cache sharding or streaming.

Llama 4 Maverick

Maverick maintains 17 billion active parameters but is built with 128 experts in a mixture-of-experts setup, totaling 400 billion parameters. This model balances performance and cost efficiency, but doesn't fit on a single GPU. It’s designed for data-center setups, where inference is deployed on multi-GPU clusters or H100 DGX hosts.

Inference Requirements:

Context Length	INT4 VRAM	FP16 VRAM
4K Tokens	~318 GB	~1.22 TB
128K Tokens	~552 GB	~1.45 TB

Training Requirements (FP16):

4K Tokens: ~8.4 TB VRAM
Designed for use in enterprise-grade compute clusters or large research environments

Running a lightweight quantized version of Maverick would require at least a 4x A100 setup with model parallelism. Even in such configurations, memory usage would be near the upper limit. The FP16 variant is considerably more demanding and generally impractical without access to datacenter-grade compute infrastructure.

Maverick also introduces improvements in modality mixing and vision understanding, making it ideal for general assistant workloads requiring reasoning over text and images. This model is best positioned for production environments where latency, precision, and scalability matter.

Llama 4 Behemoth (Predicted Requirements)

Behemoth is Meta's upcoming 2 trillion-parameter model with 288 billion active parameters. It uses the same MoE architecture as the other models but scales to a much higher ceiling, targeting STEM benchmarks and advanced reasoning tasks. While it’s still training, we can estimate its requirements based on publicly shared design parameters.

Inference Requirements (FP16, FP8):

Context Length	FP8 VRAM	FP16 VRAM
4K Tokens	~3.2 TB	~6.2 TB
128K Tokens	~4.4 TB	~7.4 TB

Training Requirements (FP8 Weights, FP16/FP32 Intermediates):

4K Tokens: ~63 TB VRAM
Requires 32,000+ GPUs and memory-sharding techniques

Behemoth pushes model scaling to the extreme. Even inference on short context lengths would require specialized infrastructure, likely involving custom scheduling and pipeline parallelism at a massive scale. Training such a model calls for tightly optimized data and model parallelism frameworks, large-scale data pipelines, and access to hundreds of PFLOPs to compute for months.

Behemoth will likely remain in research labs or internal deployments unless Meta releases distilled or reduced-size derivatives. Its size and system demands make it inaccessible to nearly all independent developers.

Conclusion

Llama 4 is designed for flexibility, but running these models, especially at scale, requires serious hardware planning. Scout offers an efficient way to experiment with long-context and multimodal capabilities on a single H100 and can support tasks like summarization or long-form analysis out of the box.

Maverick delivers better reasoning and image-text fusion performance but requires multi-GPU setups for practical use. It shines in production environments where performance-to-cost matters and infrastructure is readily available.

Behemoth remains aspirational for most developers. Until Meta releases smaller distilled variants or inference services, its scale will limit experimentation to large AI labs and cloud partners.

If you're experimenting locally, Scout INT4 is a good starting point. If you have the compute stack, Maverick offers strong multimodal capabilities for serious model deployment.

Llama 4 GPU System Requirements (Scout, Maverick, Behemoth)