24–32 GB class
L4, A10, RTX 4090 or RTX 5090. Consider for smaller models only after measuring total memory, including runtime and cache.
Infrastructure for Llama
Infrastructure for Llama
Infrastructure for Llama: model selection, memory requirements, storage and deployment choices without invented performance claims.
Choose a configuration in Console to review its resources and estimated cost.
What to plan for
Llama is a family of models. Llama 3.1 8B is an example of an 8-billion-parameter model, not a capacity promise for every Llama release.
For 8B dense weights at 16 bits, the weight-only estimate is 16 GB decimal. Runtime and KV cache need additional memory; check model licensing.
Configuration choices
48–80 GB class
L40S, A40, A100 or H100. More memory may accommodate larger working sets; it does not guarantee concurrency or token speed.
141 GB and multi-GPU
H200, B200 or B300 and explicitly sized multi-GPU systems. Confirm exact model, precision, topology and serving framework.
Weight-only memory ≈ parameters × bytes per parameter. Runtime, KV cache, activations and optimizer state are additional. This is a sizing method, not a deployment recommendation.
Smart Infra Finder
Your workload, your requirementsCheck availability: Infrastructure for Llama
Check the current price and configuration in Console. Your choice is saved through registration. The current lookup uses mock adapters; no real capacity is reserved.
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Cloud backup: access model, capacity planning and recovery considerations before choosing a configuration.
Explore →Specification sources
Hardware and model documentation; these sources do not confirm rental availability.
Official documentation 1 →