Inference Purchases Driven by Software

Winning inference deals is mostly a software and operations problem, not a chip beauty contest. A buyer cares about whether a model already runs in PyTorch, vLLM, or an OpenAI style API, how many users each card can serve at a target latency, and how much internal work it takes to keep the system stable in production. That is why FuriosaAI sells a full stack around RNGD, not just a card with strong TOPS numbers.1 2 3 4 5 6

• Nvidia’s moat comes from CUDA, TensorRT-LLM, Triton, and turnkey DGX systems, which make procurement lower risk because teams can reuse existing tools, libraries, and workflows. That pushes challengers to compete on time to deploy and cost per useful token, not raw silicon claims alone.2 7 8
• FuriosaAI is trying to remove switching pain by making RNGD usable through furiosa-torch, torch.compile, vLLM compatible serving, and OpenAI compatible endpoints. Its docs also emphasize partitioning one chip into isolated instances, which matters because better multi-tenant utilization lowers real cost per model served.1 3 4 5
• Cloud chips from AWS and Google change the comparison again. Many buyers do not want to buy and tune hardware at all, they want rented inference that already fits their cloud contracts and managed services. That shrinks the merchant chip market to workloads where an independent accelerator is clearly cheaper or easier to run.6 9 10

The next battleground is who can turn model support and serving efficiency improvements into production capacity fastest. Vendors that ship frequent SDK upgrades, broad framework compatibility, and easy deployment paths will capture more inference spend than vendors that only advertise better chip specs on paper.3 5 8