LLM Token & GPU VRAM Cost Calculator
Interactive 2026 multi-model API pricing comparison, token budget estimator, and GPU hardware VRAM sizing calculator for local and cloud inference.
[ SPECIFICATION // SECURITY ]: Multi-model token pricing estimator (Claude 3.7, GPT-4.5, DeepSeek-R1) and GPU VRAM capacity sizing calculator (FP16 vs INT4 KV-Cache).
Standard: LLM Tokenizer & Hardware VRAM Arithmetic
[ VERIFIED // LOCAL EXECUTION ][ ZERO TELEMETRY ][ OFFLINE PWA ][ OPEN SOURCE // MIT ]
[ PARAMETERS ] Token Workload
2,000 tokens
500 tokens
1,000 req/day
[ ESTIMATED COST ] Pricing Output
Monthly Estimated Cost
$405.00
Cost / Single Request
$0.01350
Cost / 1,000 Requests
$13.50
Daily Token Volume
2.5M Tok
[ NOTE ] Prompt Caching Benefit: For models supporting prompt caching (Anthropic, DeepSeek, Google), cache-hit tokens typically yield 75% to 90% discount on input pricing.
[ BENCHMARK ] Multi-Model Cost Matrix (Monthly at Selected Volume)
| Model | Provider | Input $/1M | Output $/1M | Cost / Req | Monthly Cost |
|---|
[ ARCHITECTURE ] Model & KV-Cache Configuration
32,768 tokens
4 concurrent streams
[ HARDWARE SPEC ] Required VRAM & Sizing
Total VRAM Required
7.8 GB
Model Weights VRAM
4.0 GB
KV-Cache Memory
2.3 GB
CUDA Context & Overhead
1.5 GB
Recommended GPU Hardware Tier:
1x RTX 4060 Ti 16GB / Apple M3 24GB
[ SIZING FORMULA ]: Total VRAM = (Weights_GB) + (2 × n_layers × n_kv_heads × d_head × seq_len × batch_size × kv_bytes) + CUDA_Context_Buffer.
[ INTERACTIVE ] In-Browser Subword Token Boundary Highlighter
Type or paste any prompt below to visualize subword splitting, token boundaries, and approximate token counts in real-time.
Tokens: 23
Characters: 149
Bytes: 149 B
Avg Chars/Token: 6.5
Visualized Token Chunks:
[ RELATED TECHNICAL GUIDES ] Deep-Dive References
[ HIGH-THROUGHPUT AI SERVING ]
vLLM PagedAttention & KV-Cache INT4 Quantization Guide
Optimize LLM inference throughput, manage KV-Cache memory fragmentation using PagedAttention, and configure tensor parallelism.
Frequently Asked Questions (FAQ)
How do I calculate VRAM requirements for running LLM models?
Minimum VRAM is computed from model parameter weights (e.g. 7B FP16 = ~14GB, INT4 = ~4GB) plus KV-Cache allocations (based on context length and batch size) and CUDA runtime overhead (~1.5GB).
How many tokens are produced per 1,000 words of text?
On average, 1,000 English words yield approximately 1,300 to 1,400 tokens based on modern BPE tokenizers used by GPT, Claude, and Llama architectures.
What is the impact of INT4 / AWQ quantization on memory and model quality?
INT4 quantization reduces VRAM footprint by 60-70% with negligible perplexity degradation (<1-2% on modern 70B+ architectures), enabling execution on consumer GPUs.
Does this calculator include latest 2026 AI model pricing presets?
Yes, the calculator includes updated per-million token pricing for Claude 3.7 Sonnet, GPT-4.5, DeepSeek-R1, Gemini 2.0 Flash, and open-weights Llama 3.3 models.
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