NVIDIA wins mixed AI, rendering stacks

Hot take: if this team wants one platform that “just works” across inference, training, and graphics, NVIDIA is the better default; if the primary constraint is maximum VRAM at minimum cost and you can absorb stack friction, AMD is the aggressive value play.

• –vLLM does support multi-GPU serving with tensor parallelism, so splitting a model across GPUs is a normal use case; on a single node, you set `tensor_parallel_size` to the GPU count, and beyond one machine vLLM leans on Ray and pipeline parallelism, which is explicitly more complex and partly beta.
• –Ten small GPUs is not the same as two big GPUs: you gain total memory capacity, but you also increase coordination overhead, failure surface area, thermal/power complexity, and scheduling overhead for concurrent users.
• –For PyTorch training and general ML tooling, NVIDIA still has the cleaner path because CUDA remains the safest ecosystem bet; AMD ROCm has improved, but its own docs still call out multi-GPU limitations and narrower Windows support.
• –For rendering and ray tracing, NVIDIA is the stronger choice because the RTX Pro line is built around CUDA and RT cores, and that ecosystem advantage matters if “some raytracing” is a real requirement rather than a nice-to-have.
• –The AMD option is attractive on paper because 10×32 GB creates a lot of aggregate VRAM headroom, which is useful for concurrency and larger context windows, but that only pays off if your software stack can reliably exploit it.
• –For a 70-person company, operational simplicity usually beats raw capacity: fewer GPUs with better software support often means less time spent debugging the platform and more time spent using it.