4,096 stream processors against 4,352 CUDA cores looks like a close race. It is not a comparison at all. The two vendors count different units of execution hardware, group them into different SIMD widths, clock them differently, and expose them through different programming models — so the headline number on an AMD spec sheet and the headline number on an NVIDIA spec sheet are not the same quantity measured twice.
Why can’t AMD stream processor counts be compared to NVIDIA CUDA core counts?
Because neither figure is a throughput figure. Both are counts of scalar lanes, but a lane only does work when the scheduler can keep it fed, and the two architectures differ in how lanes are grouped into a wavefront or warp, how many registers back each lane, and how much shared memory a resident workgroup can hold. Occupancy under your kernel’s register and shared-memory pressure decides how many of those lanes are busy, and a bandwidth-bound kernel will leave most of them idle on either vendor.
There is a second, harder constraint: reachability. CUDA cores are only addressable through NVIDIA’s stack, and stream processors only approach peak throughput under an AMD-aware memory access pattern. A core you cannot target with the toolchain you standardised on contributes nothing, whatever the spec sheet says. That makes the core-count question the hardware-side face of the compute API decision, not a separate one.
Why benchmarking beats spec sheets
| Instead of | Compare | Why it decides the purchase |
|---|---|---|
| Core / stream-processor count | Sustained FP32, FP16 and INT8 throughput at the precision the model actually uses | Precision choice moves achievable throughput more than lane count does |
| Peak theoretical FLOPS | Effective memory bandwidth on your access pattern | Most inference and many rendering kernels are bandwidth-bound |
| Clock speed | Occupancy under your kernel’s register and shared-memory pressure | Idle lanes cost the same as absent lanes |
| Tensor / matrix / RT core counts | Measured time on the specific operator mix you run | These units only help if your workload dispatches to them |
| Vendor benchmark charts | Cost per inference or per rendered frame on each candidate part | This is the number procurement can defend |
Vendor-supplied core counts tell you almost nothing about whether your particular algorithm will run faster on NVIDIA or AMD hardware. In our experience, spec-sheet rankings invert for a specific workload often enough that skipping this step is the expensive option, particularly where a team sizes a cluster on core counts and only discovers post-deployment that the kernel was bandwidth-bound all along (observed across TechnoLynx engagements; not a published benchmark).
We work through the same reasoning in our GPU acceleration and optimisation engineering practice, where hardware fit is expressed as measured throughput and bandwidth for the customer’s own kernels rather than vendor core counts.
So the question worth asking a vendor is not how many cores the part has, but what fraction of them your kernel can keep busy — and whether anyone has measured it.
