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AI LLM Time-to-First-Token (TTFT) & Throughput Estimator (2026)

Estimate prefill Time-To-First-Token (TTFT), decoding tokens per second (TPS), inter-token latency (ITL), and end-to-end response time across GPU architectures and model sizes.Documentation & FAQs ↓

AI LLM Time-to-First-Token (TTFT) & Throughput Estimator — Interactive Console
Runs locally in your browser • Instant output
Time-To-First-Token (TTFT)
45 ms
Decoding Speed
189.0 t/s
Inter-Token Latency
5.3 ms
Total Turn Time
1.63s
Ready
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2026 Quick-Reference Cheat Sheet & Benchmark Table: AI LLM Time-to-First-Token (TTFT) & Throughput Estimator

Quick Answer & 2026 Technical Summary (llm ttft calculator tokens per second)Updated 2026 Standard

TTFT is the duration between sending a prompt and receiving the first generated token, representing prompt ingestion and prefill computation. Use this interactive llm ttft calculator tokens per second above to test time to first token estimator, llm inference throughput calculator, and tokens per second llm locally in your browser with zero server uploads.

Target Keyword Spec: llm ttft calculator tokens per second | Modules: Prefill vs Decode Latency • GPU Hardware Presets • Quantization & KV Cache Impact
Primary Focus: llm ttft calculator tokens per second
Core Capability: time to first token estimator
Privacy Mode: 100% Client-Side (Zero Upload)
Technical Parameter / ModuleStandard / Keyword SpecArchitecture & Validation RuleOperational Use Case (2026)
Prefill vs Decode Latencytime to first token estimatorSeparates compute-bound prompt prefill (TTFT) from memory-bandwidth-bound t...AI Application Architecture
GPU Hardware Presetsllm inference throughput calculatorPre-configured bandwidth specs for NVIDIA H100, A100, RTX 4090, Apple M4 Ma...GPU Cloud Provisioning
Quantization & KV Cache Impacttokens per second llmEvaluates FP16, Q8_0, Q4_K_M, and KV cache memory constraints on throughput.Prompt Engineering Optimization
Tokenizer & Model Architecturetiktoken (o200k_base / cl100k_base) + GGUF1 Token ≈ 0.75 English Words (~4 Chars)Calibrated for 2026 Frontier & Open-Weight LLMs
Context Window & KV Cache Scaling8k / 32k / 128k / 1M+ Token ContextsFP16 vs Q8_0 vs Q4_K_M QuantizationAccounts for FlashAttention & prompt caching
Inference Cost & Throughput MetricUSD per 1M Input / Cached / Output TokensMemory Bandwidth (GB/s) ÷ Model Size (GB)Optimizes self-hosted GPU vs cloud API ROI

Step-by-Step Workflow

4 Easy Steps
01Phase 1

Select Model Size

Choose model parameter count (e.g. 8B, 14B, 70B, or 671B MoE) and quantization tier.

02Phase 2

Choose Hardware Accelerator

Select your GPU or inference chip to load memory bandwidth (GB/s) and TFLOPS.

03Phase 3

Set Prompt & Output Tokens

Specify prompt input length (e.g. 1,000 tokens) and expected generation length (e.g. 300 tokens).

04Phase 4

Review Performance Estimates

Analyze estimated TTFT, decoding tokens/sec, and total end-to-end latency.

Real-World Applications

AI Application Architecture

Benchmark whether an AI agent can meet sub-500ms conversational voice or chat latency SLAs.

GPU Cloud Provisioning

Select the most cost-effective GPU cluster to achieve desired tokens-per-second targets.

Prompt Engineering Optimization

Quantify how trimming system prompts improves initial response responsiveness.

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