Model advisor
Three open-source models from arena.ai/leaderboard/agent (Open Source filter). For each one, the memory math is shown — not hidden — followed by the cheapest real hardware that actually clears that bar. We never recommend hardware that doesn't have enough memory to hold the model.
InteractiveCan my model run?
Type any parameter count and every product in the catalog is checked at once — real memory math, real prices, real power draw, no fixed list of models required.
Or skip the list and set both fields yourself:
Or start from one of these 3 leaderboard models
Pre-analyzed with the same real memory math, if you'd rather not type a number.
Qwen3.5-397B
397B parameters.
1x 8x H100 SXM server
DeepSeek-V3.2
671B total parameters (37B active at a time — a mixture of expertsiAn AI model design that splits its knowledge into many specialized "expert" sub-networks. Only a handful of experts activate for any given word, so it computes faster than its size suggests — but all the experts still have to be sitting in memory, ready to be picked. model, so only part of it fires per word, but all of it still has to sit in memory).
1x 8x H200 HGX server
Kimi K2.6
1000B total parameters (32B active at a time — a mixture of expertsiAn AI model design that splits its knowledge into many specialized "expert" sub-networks. Only a handful of experts activate for any given word, so it computes faster than its size suggests — but all the experts still have to be sitting in memory, ready to be picked. model, so only part of it fires per word, but all of it still has to sit in memory).
2x 8x H100 SXM server
A "cluster" just means multiple computers wired together and coordinated so they behave like a single, bigger machine. Here, 2x 8x H100 SXM server units are combined to reach 1,280 GB of memory.
Real deal vs. just plugging in cards: the GPUs inside each server are already wired together with NVLinkiNVIDIA's high-speed direct connection between GPUs in the same server — much faster than a normal PCIe slot connection, so the GPUs can share memory and work almost as if they were one big GPU. for near-instant, high-bandwidth memory sharing. Connecting multiple servers into one cluster additionally needs InfiniBandiA high-speed networking technology used to connect separate servers together in a datacenter cluster — the equivalent of NVLink, but between whole machines instead of GPUs on one board. networking between them — dedicated high-speed cabling and switches built for this job. That's very different from buying several desktop cards and plugging them into one PC over ordinary PCIe: technically possible, but far slower for a single huge model, and not something a datacenter would do at this scale. This estimate also doesn't include the extra cost of that networking gear.