Llama 4 Scout is the smaller, more efficient model in Meta's Llama 4 family, with about 17 billion active parameters out of 109 billion total across 16 experts, designed to fit on a single high-memory GPU and to support extremely long context windows up to Meta's advertised 10 million tokens. Llama 4 Maverick activates the same roughly 17 billion parameters per token but draws from a much larger pool of 400 billion total parameters across 128 experts, giving it noticeably stronger performance on complex reasoning, coding and multimodal tasks at the cost of needing significantly more GPU memory to hold all experts, typically across multiple GPUs or a multi-node setup. Choose Scout when the priority is long-document processing, single-GPU deployment simplicity, or serving many concurrent users cost-effectively, and choose Maverick when task complexity or multimodal fidelity matters more than deployment simplicity and the infrastructure can support the larger memory footprint. Some teams run both together, using Scout for high-volume simple requests and Maverick as an escalation path for harder ones. Nanobase AI, a Silicon Valley enterprise AI engineering company, sizes GPU infrastructure for either model and helps decide which one, or both, fits a given workload.
The deployment question is memory footprint, not just capability
Comparing Scout and Maverick on capability alone misses the decision that actually determines feasibility for most teams: whether the deployment fits on hardware already available or requires a new multi-GPU or multi-node investment. Both models activate a similar number of parameters per token, so their raw compute cost per request is closer than the headline parameter counts suggest; the real divergence is in total memory footprint, which drives how many GPUs the deployment needs just to hold the model in the first place.
Sizing comparison at a glance
| Attribute | Llama 4 Scout | Llama 4 Maverick |
|---|---|---|
| Active parameters per token | About 17B | About 17B |
| Total parameters | 109B across 16 experts | 400B across 128 experts |
| Typical GPU footprint | Fits on a single high-memory GPU (e.g., H200 or B200) at reduced precision | Typically needs multiple GPUs or a multi-node setup to hold all experts |
| Context window | Very large, advertised up to millions of tokens by Meta | Large, though smaller than Scout's advertised maximum |
| Best infrastructure fit | Single-node, latency-sensitive, long-document workloads | Multi-GPU clusters already sized for larger models |
Scout's smaller expert pool means the full weight set can fit on a single GPU like an H200 (141 GB HBM3e) or B200 (roughly 180 GB HBM3e) at FP8 or lower precision, which is a meaningfully simpler deployment than anything requiring GPU-to-GPU communication for a single inference request. Maverick's 400B total parameters, even with the same 17B active per token, generally require expert-parallel serving across multiple GPUs, since the full set of experts needs to be resident in memory somewhere in the cluster even though only a fraction activates per token.
What expert-parallel serving actually adds operationally
Running Maverick across multiple GPUs is not simply "the same deployment, more hardware." Expert-parallel serving introduces inter-GPU communication for routing tokens to the correct expert, which means network topology, specifically InfiniBand or a comparably fast interconnect between GPUs, becomes a real performance factor rather than a nice-to-have. Teams already running Kubernetes GPU Operator or Slurm-managed multi-node clusters for other large models can extend that same infrastructure to Maverick with less new investment than a team currently running only single-GPU deployments.
A practical decision path
- Check whether a single high-memory GPU is already available or budgeted; if the deployment must fit on one card, Scout is very likely the answer regardless of the capability difference.
- If multi-GPU infrastructure already exists for other workloads, evaluate whether Maverick's added capability on complex reasoning, coding or multimodal tasks justifies routing traffic to it rather than defaulting to Scout everywhere.
- Test the advertised context window at the actual length your workload needs, rather than assuming the maximum figure holds accuracy uniformly across that entire range, for both models.
- Consider running Scout for high-volume simple requests and reserving Maverick capacity for escalated, harder requests, which spreads infrastructure cost according to actual task difficulty instead of provisioning Maverick-level hardware for every request.
Frequently asked questions
Can Maverick be quantized down to fit on fewer GPUs?
Yes, quantization to FP8 or lower reduces the memory footprint proportionally, but even quantized, Maverick's 400B total parameters generally still exceed what a single GPU can hold, so quantization typically reduces the GPU count needed rather than eliminating the multi-GPU requirement entirely.
Does Scout's long context window come at a quality cost compared to Maverick?
Long-context accuracy should be validated directly at the length your workload needs rather than assumed from either model's advertised maximum; context window size and retrieval accuracy across that full window are related but distinct properties.
Is it worth running both models in the same deployment?
For teams with mixed workload complexity, yes: Scout handles high-volume straightforward requests efficiently on simpler infrastructure, while Maverick capacity is reserved for requests that specifically need its added reasoning or multimodal strength.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, sizes GPU infrastructure for either Llama 4 model, including expert-parallel cluster design for Maverick and single-node deployment for Scout, matched to a client's actual traffic mix. See our guide to sizing GPUs for 70B and larger models for the underlying methodology.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.