Is DeepSeek V3 cheaper to run than Llama 4 at scale?

DeepSeek V3 and Llama 4 Maverick have similar active-parameter compute cost per token, since V3 activates about 37 billion of its 671 billion parameters per token while Maverick activates around 17 billion of its 400 billion, meaning neither has a dramatic per-token compute advantage over the other once served efficiently. Where they differ most is total memory footprint: DeepSeek V3's much larger total parameter count means it needs more aggregate GPU memory across a cluster to hold all experts, which raises the hardware investment required before any inference happens, even though its actual compute per token is competitive. Once that hardware is in place and running at good utilization, both models achieve low marginal cost per token because of their mixture-of-experts efficiency compared to an equivalently capable dense model. The practical cost comparison depends more on a specific hardware configuration, batching strategy and achieved GPU utilization than on a simple parameter count comparison between the two models. Independent published throughput benchmarks on identical hardware are the only reliable way to compare real serving cost between them for a given workload. Nanobase AI benchmarks both models on identical GPU configurations to give clients an accurate cost-per-token comparison before committing to hardware.

Separate the two numbers that actually drive cost

Comparing these two mixture-of-experts models by a single "cost to run" figure hides that two different numbers matter, and they point in different directions. Active parameters per token drive the compute cost of generating each token, while total parameters drive how much GPU memory the cluster needs just to hold the model at all, and DeepSeek V3 and Llama 4 Maverick diverge sharply on the second number while staying close on the first.

Architecture comparison

DeepSeek V3 actually activates more parameters per token than Maverick, roughly 37B versus 17B, which narrows the common assumption that V3 is the more compute-efficient of the two on a strict per-token basis. Where V3's much larger total parameter count matters is memory: holding all 671B parameters, even at a reduced precision like FP8, requires substantially more aggregate GPU memory across the cluster than Maverick's 400B total, before any inference throughput is even considered.

What this means for cluster sizing in practice

A rough sense of scale is useful here without treating it as precise: 671B parameters at one byte per parameter in FP8 works out to roughly 670 GB of weight memory alone, before KV cache and activation memory are added, which typically means spreading the model across multiple nodes of 80 GB or larger class GPUs (H100, H200, or B200) connected by a fast interconnect such as InfiniBand. Maverick's 400B total parameters follow the same logic at a smaller scale, roughly 400 GB of FP8 weight memory, still generally requiring a multi-GPU or multi-node setup but with meaningfully less aggregate memory to provision than DeepSeek V3.

Why per-token compute cost does not translate directly to serving cost

Once the memory footprint is provisioned and the cluster is running, actual serving cost per token depends heavily on achieved GPU utilization, batching efficiency, and how well the serving framework, vLLM, TensorRT-LLM, or NVIDIA NIM, handles the expert-routing communication overhead that any mixture-of-experts model introduces across GPUs. Two models with similar active-parameter counts can still show different real-world throughput if one's expert routing pattern creates more inter-GPU communication overhead than the other under a given hardware topology. Neither active-parameter count nor total-parameter count alone predicts real serving cost; only a throughput benchmark on the specific target hardware configuration does, which is why published architecture specs should inform planning, not substitute for a benchmark.

Frequently asked questions

Does DeepSeek V3's larger total parameter count mean it is always more expensive to run?

Not necessarily at the per-token level, since compute cost tracks active parameters, where V3 and Maverick are fairly close. The larger expense from V3's size shows up specifically in the upfront GPU memory investment needed to hold the full model, which is a capital and infrastructure planning cost distinct from ongoing per-token serving cost.

Can either model run efficiently on a single node?

Both generally require a multi-GPU setup given their total parameter counts, though the exact node count depends on the GPU model chosen, the precision used, and whether the deployment targets throughput or just functional correctness at minimal hardware.

Is there a published, reliable head-to-head cost benchmark between the two?

Reliable comparisons depend on identical hardware, identical serving software versions, and matched batching configuration, which vary between any two independent benchmarks; running both models on your own target hardware configuration remains the only way to get a cost figure that applies to your specific deployment.

How Nanobase AI helps

Nanobase AI benchmarks both models on identical GPU configurations to give clients an accurate cost-per-token comparison before committing to hardware, accounting for the memory footprint and expert-routing overhead that architecture specs alone do not reveal. See our guide to sizing GPUs for 70B and larger models for the underlying sizing methodology.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.