Attention
Kimi-K3 KDA Data-Flow Visualizer
Animated walkthrough of Kimi Delta Attention (KDA) in Kimi-K3. Configure num_heads, head_dim,
hidden_size, and TP to recompute every tensor shape and dtype live, then step through the
four execution paths — prefill (chunk/FlashKDA), decode (fused_recurrent), fused
decode (single CUDA kernel), and spec decode. Highlights the conv_state and
recurrent_state caches as data flows through fused input projection → conv1d → delta-rule → output gate.
Memory
KV Tier Sizing Calculator
Closed-form diagnostic for LLM KV-cache tiers (L1 device / L2 host / L3 store). From hardware,
model, and multi-turn workload, decide whether serving is compute-bound or
KV-movement-bound via two hit rates — theoretical (1 − new/total_len) vs fill-threshold
(1 − chunk/P). Computes the L1 KV pool, useful vs useless L1, prefill/decode max-req, load-back
overlap ratio, and where L3 pays off. Distilled from the L3 KV-tier study.
System
PD Disaggregation Simulator
Interactive simulation of Prefill-Decode disaggregated LLM serving with DEP (Data-Expert Parallelism).
Visualize producer-consumer queue dynamics, configure prefill/decode node counts, DEP ranks, TPOT,
and request parameters. Real-time charts show queue depths and decode utilization over time.
Agent
Agent PD Simulator
Discrete-event simulation of multi-turn agent serving with PD disaggregation,
UMBP KV prefetch vs recompute decisions, tiered memory offload (HBM/DRAM/SSD),
and animated particle visualization of request and KV data flows.
Includes embedded break-even cost model formulas.
Serving
Agent Serving Pareto Explorer
Interactive visualization of throughput–latency Pareto trade-offs for multi-turn agent workloads.
Explore how concurrent sessions, inter-turn pause time, prefix caching, and context growth
reshape the serving performance frontier. Based on closed-loop queuing theory and
operator-level performance modeling inspired by AIConfigurator.
Numerics
Float Toy
Interactive IEEE 754 floating-point visualizer. Click individual bits to see how sign, exponent,
and mantissa fields combine to represent FP64, FP32, FP16, BF16, FP8, and FP4 (MXFP4/NVFP4 with block scale) numbers.
Useful for understanding quantization trade-offs in LLM inference.