How Much GPU VRAM Do AI Agents Need?

AI agent VRAM requirements depend primarily on the underlying model rather than the agent orchestration layer, with model parameter count, numerical precision, context length, concurrent sessions, and runtime overhead all contributing to memory use. Model weights in FP16 or BF16 generally require about two bytes per parameter, while 8-bit or 4-bit quantization can substantially reduce memory requirements, though potentially with some quality tradeoffs. Agents also require GPU memory for KV caches that retain processed context, and these caches can grow significantly with long inputs, files, retrieval results, or multiple simultaneous users. GPU selection should therefore account for model weights, anticipated context and concurrency, a 10%–20% overhead buffer, and compute performance and memory bandwidth in addition to price. Suggested ranges span roughly 8–16 GB for simple 7B–9B model automation, 16–24 GB for standard workflows, 24–48 GB for complex coding and planning, 48–80 GB for advanced long-context workloads, and more than 80 GB or multiple GPUs for very large or multi-agent deployments.

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