Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.xhipai.com/llms.txt

Use this file to discover all available pages before exploring further.

​
Latency Estimator

Physics-based latency modeling for LLM inference. Captures the two fundamental bottlenecks: memory bandwidth (decode) and compute (prefill).

​
estimateTpot(arch, contextTokens, batchSize, hardware, kvPrecision?)

Time Per Output Token — each decode step streams the full KV cache from HBM. 
import { estimateTpot, DEFAULT_ARCHITECTURES, DEFAULT_GPU_SPECS } from "@radaros/core";

const tpot = estimateTpot(
  DEFAULT_ARCHITECTURES["llama-3.1-70b"],
  4096,    // 4K context
  1,       // batch size
  { gpu: DEFAULT_GPU_SPECS["h100-sxm"], gpuCount: 4, nandPerGpuGb: 0, nandBandwidthGBs: 7 },
  "fp8",
);
// → milliseconds per output token
TPOT scales linearly with context length and batch size. fp8 halves it vs bf16.

​
estimateTtft(arch, promptTokens, concurrentUsers, hardware)

Time To First Token — compute-bound prefill with queue congestion. 
import { estimateTtft } from "@radaros/core";

// Single user
estimateTtft(llama70b, 4096, 1, hw);   // fast — just one prefill

// 20 concurrent users
estimateTtft(llama70b, 4096, 20, hw);  // 10× worse — queue serialization
The model uses TTFT(C) = singlePrefill × (C+1)/2 — with C concurrent users, the average user waits for C/2 prefills ahead of them.

​
singlePrefillMs(arch, promptTokens, hardware)

Base prefill time for a single prompt with no queue contention. 
import { singlePrefillMs } from "@radaros/core";

singlePrefillMs(llama70b, 4096, hw);    // ~16ms on 4× H100
singlePrefillMs(llama70b, 32768, hw);   // much longer — quadratic attention
Prefill is compute-bound: flops = (4 × N² × hiddenDim + 4 × N × ffnDim) × layers.

​
ttftBreachPoint(arch, hardware, ttftSlaMs, promptTokens?)

Maximum concurrent users before TTFT exceeds the SLA. 
import { ttftBreachPoint } from "@radaros/core";

const breach = ttftBreachPoint(llama70b, hw, 5000, 4096);
// → e.g. 629 users before 5s SLA breach

// More GPUs push the breach point out
const breach8gpu = ttftBreachPoint(llama70b, { ...hw, gpuCount: 8 }, 5000);
// → higher number — more TFLOPS
Adding NAND does not change the breach point — NAND doesn’t help prefill compute.

​
restoreLatency(kvSizeGb, nandBandwidthGBs, parallelism?)

Time to restore a cold session from NAND SSD to GPU HBM. 
import { restoreLatency } from "@radaros/core";

restoreLatency(40, 7);     // 40 GB at Gen4 (7 GB/s) → 5,714ms
restoreLatency(40, 14);    // 40 GB at Gen5 (14 GB/s) → 2,857ms
restoreLatency(40, 7, 4);  // 4 parallel streams → 22,857ms per stream
Parallel restore streams share the same SSD pipe — higher parallelism helps throughput but increases per-stream latency.