feed/tags/hardware
hardware
One explainer you can run, 15 glossary terms, and 3 readings ordered as a path, each with one line on why it earns your time.
- High bandwidth memory
The stacked DRAM on the GPU package that holds the weights and the KV cache.
- Memory bandwidth
Bytes per second the GPU can move between its HBM and its compute units.
- Roofline model
A chart with arithmetic intensity on the x axis and attainable performance on the y, bounded by a sloped line from memory bandwidth and a flat line from peak compute.
- Arithmetic intensity
Floating-point operations performed per byte moved from memory.
- Ridge point
The arithmetic intensity at which a machine's memory ceiling and compute ceiling meet: peak FLOPS divided by bandwidth.
- Memory-bound
Limited by how fast bytes can be read, not by how fast they can be operated on.
- Compute-bound
Limited by arithmetic throughput; the memory system keeps up and the tensor cores are the bottleneck.
- Tensor cores
The GPU units that do small dense matrix multiplies in one instruction, at far higher rate than the general-purpose cores.
- FP8
An 8-bit float in two flavours: E4M3 with more precision for weights and activations, E5M2 with more range for gradients.
- FP4
A 4-bit float with sixteen representable values, usable only with fine-grained scaling: a shared scale factor per small block of values restores the range the format lacks.
- Tensor parallelism
Splitting each weight matrix across GPUs so that every layer's matmul runs on all of them at once, with an all-reduce to combine the partial results.
- Wide expert parallelism
Expert parallelism stretched across many GPUs, often a whole rack, so each card holds one or two experts and the batch of tokens reaching each is large enough to make its GEMM efficient.
- NVLink
NVIDIA's GPU-to-GPU interconnect, an order of magnitude faster than PCIe, and the switch fabric that joins every GPU in a node or rack to every other at full rate.
- Interconnect bandwidth
Bytes per second between GPUs, whether over NVLink inside a node, InfiniBand or Ethernet between nodes, or PCIe to the host.
- TensorRT-LLM
NVIDIA's inference library for its own GPUs: compiled engines with hand-tuned kernels, the first place FP8 and FP4 land, in-flight batching, paged KV cache, and the wide expert parallelism and disaggregated serving that its newest racks are built for.
The mental models the rest of the field takes for granted.
Current work, at the depth practitioners actually argue about.
- articleOptimizing on-device inference for Apple silicon
The roofline on one laptop. Decode hits 90% of the chip's sustained weight-read rate and removing the arithmetic changes nothing, so speculative decoding comes out 18% slower at batch 1.
Perplexity Engineering#decode#prefill#quantization#kernels#hardware - articleAgentX and InferenceX v3: does the CUDA moat hold up in agentic inference?
An agent turn is 88K tokens in and 413 out, so the benchmark is really a KV cache hit-rate benchmark: 91% on B300 at 384 sessions, 73% on B200 with the rest spilling to DRAM. Nvidia, AMD, five open models, 70 upstream PRs. Partly paywalled.
Cam Quilici, Bryan Shan, Alec Ibarra and others#agents#prefix-cache#kv-cache#benchmark#hardware