GPU Inference Cost Calculator

Estimate what it actually costs to run an LLM in production on GPU clouds — cost per 1M tokens and monthly cost — before you commit to a provider.

Pick your model, quantization and workload below. The calculator compares RunPod, DigitalOcean and Lambda and tells you which GPUs fit in memory and which is cheapest for your traffic.

How the estimate works

This is a first-order, memory-bandwidth-bound model, not a benchmark. It’s designed to give you a defensible ballpark in seconds, not a vendor quote.

• Weights = parameters × bytes per parameter (FP16/BF16 = 2, INT8 = 1, INT4 = 0.5)
• KV cache ≈ 4 KB per token per 1B parameters (heuristic — varies by model and context)
• Decode throughput ≈ GPU memory bandwidth ÷ weights (GB) — memory-bound decode, scaled by an efficiency factor (default 70%)
• Cost per 1M tokens = ($/hr ÷ (tok/s × 3600)) × 1,000,000

Methodology & disclaimer

• Prices are on-demand list prices captured September 2026 and change frequently. Always confirm on the provider’s pricing page before committing.
• The model assumes memory-bound decode (the dominant cost for serving). Prefill, batching, speculative decoding and multi-GPU tensor parallelism are not modeled — real throughput can be higher (batching) or lower (small batches, overhead).
• “Fits?” is a rough VRAM check (weights + KV cache vs GPU memory). It does not account for CUDA overhead, activation memory or framework buffers.
• This is an estimate for planning, not a benchmark. For real numbers, see our benchmark articles.

Affiliate disclosure

RunPod and DigitalOcean links on this page are affiliate links — if you sign up through them, RavChat may earn a commission at no extra cost to you. This does not affect the numbers shown. Lambda is not an affiliate partner.

Newsletter

Want the real numbers behind these estimates? The RavChat Inference Brief covers one benchmark, one cost change, one new model and one production lesson every week.