Blackwell Tensor Memory and matmul accumulation
The post describes how Blackwell's Tensor Memory, a separate 256 KB space per SM, stores Tensor Core results and reduces, according to the author, the issue of a matmul from 128 threads to one.
Source: Como Blackwell move a acumulação de matmul para Tensor Memory (tcgen05.mma). Text prepared with AI from this source.
What happened and what to do
The post describes Tensor Memory in Blackwell as a separate 256 KB space per SM, dedicated to Tensor Core results. According to the author, this separation allows the issue of a matmul operation to drop from 128 threads to one. The text explains how this changes accumulator storage and the thread model for matmul on the GPU.
For a company running inference or training on Blackwell GPUs, the change matters mainly for custom kernels and for libraries that depend on matmul. A practical response is to measure before changing: profile the most expensive kernels, compare latency and throughput on real workloads, and check whether the libraries in use already take advantage of Tensor Memory. This work can be built as a reproducible benchmark pipeline with a per-model metrics dashboard, running on the client's own infrastructure.
How the consultancy can help
Performance diagnosis of matmul workloads on Blackwell GPUs: kernel profiling, comparison of libraries and estimate of possible gains. Then, implementation of a reproducible benchmark and a monitoring dashboard, with continuous operation on the client's infrastructure.
Next step
If your team runs models on Blackwell GPUs and wants to find the bottleneck, send a short description of your case, including hardware, workloads and goal. We will return a scoped proposal for diagnosis and implementation.
Let's assess your project
I reply with scope and a proposal.