Analysis outputs

analyze(doc, options) returns every number at once. validate(doc, options) returns the same thing with design-rule findings attached.

Nothing in the UI computes its own numbers: one call per document and operating point feeds every panel.

The operating point

Conditions the design is measured under, never properties of it:

Option	Meaning
B	Micro-batch size
T	Sequence length in tokens
bytes	Bytes per element of the activation dtype
hardware	GPU profile id
gpus	How many
sharding	ZeRO stage, tensor and pipeline parallel degree
recompute	none / selective / full
tokens	Training token budget, for cost and Chinchilla

params

total, active, byPath, byCategory.

Sparse designs differ in the two totals: a container carries a total multiplier that drives the parameter count and an active multiplier that drives FLOPs. That single mechanism is what makes mixture-of-experts work.

byPath holds leaf blocks only. A container's contribution is the sum of the paths beneath it.

flops

Per token, unless stated.

Field	Meaning
fwdDense	Matmul FLOPs that do not depend on sequence length
fwdAttention	Score and value products at the given T
fwdAttentionUnmasked	The same counted as if nothing were masked
fwdTotal	fwdDense + fwdAttention — what a fused causal kernel does
fwdTotalUnmasked	What a profiler reports
elementwise	Norms, activations, RoPE, residual adds — memory-bound, excluded above
trainPerToken	Forward plus backward, plus recomputation
attentionShare	Fraction of forward FLOPs inside attention
ruleOfThumb2N / ruleOfThumb6N	The usual approximations, for comparison

The two totals exist because they are answers to different questions. See Verify a design against PyTorch.

For a convolutional design the token is one image, so T is 1 and every per-token figure reads as per-image.

kv

bytesPerToken, and the total at the current batch and sequence length. A state-space layer keeps a fixed state per sequence rather than one growing with every token, which is most of why hybrid stacks are cheap to serve.

memory

Weights, gradients, optimiser state and activations, before and after sharding, per GPU and in total. Activation figures follow the Megatron formulas.

Activation memory is attributed to tensors, not blocks: a block declares which inputs it must keep alive, and each producing tensor is counted once even when several blocks read it.

throughput

A roofline estimate: arithmetic intensity against the chosen hardware's compute and bandwidth, and which of the two binds.

cost and chinchilla

Training cost at the given token budget, and how far that budget is from compute-optimal — about twenty tokens per parameter.

symbols, infer, expanded, flat

The intermediate results, exposed because the UI needs them: the resolved symbol table, shapes before and after composite expansion, and the flattened list of primitive instances the totals are summed from.