Model Gallery
Release timeline — model size over time
Each point is a model at its release date (X) vs total params (Y, log). Point labels mark activated params/token; colour = family. Models without a recorded release date are omitted.
Model size — total vs activated params
Each point is a model. Dense models have active = total (upper envelope); MoE models fall well below (only a fraction of experts fire per token). X is log, Y is linear.
Per-request KV cache — BF16
Total KV cache one request occupies vs context length, assuming a BF16 cache. Architecture only — independent of GPU/parallelism. MLA (DeepSeek) and sliding-window/linear backbones grow far slower than full GQA/MHA.
Prefill TTFT — SOL, batch size 1
Time-to-first-token vs context length. One line per model.
Decode TPOT — SOL latency
Time-per-output-token vs context length, at fixed batch size (lower is better). One line per model.
Decode throughput — SOL, batch size = max
Tokens/s vs context length at each model's KV-bound max batch (higher is better). TPOT isn't comparable here — each model runs a different max batch — so throughput (= effective tokens/step ÷ TPOT) is the apples-to-apples decode metric. Gaps = no request fits at that context.