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The Self-Hosted Model Cost calculator estimates the total expense of running an open-source LLM (Llama 3, Mistral, Mixtral, Phi-3) on your own infrastructure, including GPU servers, bandwidth, storage, and operational overhead. It compares self-hosting against equivalent API costs to find the breakeven point.

Wzór

Monthly Hosting Cost = GPU Server Cost + Storage + Bandwidth + Ops Overhead

C_gpu: GPU Server Cost ($/month) — Monthly GPU rental or amortized hardware cost
C_ops: Operations Overhead ($/month) — Engineering time, monitoring, and maintenance costs
Q: Monthly Queries (queries/month) — Total inference requests served per month
BW: Bandwidth Cost ($/month) — Data transfer costs for serving responses

Przewodnik krok po kroku

1Select the model you want to host and its hardware requirements
2Choose between cloud GPU rental (Lambda, RunPod, AWS) or on-premise hardware
3Enter your expected query volume and concurrent user load
4View monthly cost, cost per query, and breakeven vs. API alternatives

Rozwiązane przykłady

Wejście

Llama 3 70B on 2× A100 80GB (Lambda Labs), 200K queries/month

Wynik

GPU: 2 × $1.10/hr × 730 hrs = $1,606/month. Storage: $50. Bandwidth: $100. Ops: $200. Total: $1,956/month. Per-query: $0.0098. Breakeven vs. GPT-4o at $0.005/query: self-hosting is more expensive at this volume.

Wejście

Mistral 7B on 1× A10G (AWS), 1M queries/month

Wynik

GPU: $0.76/hr × 730 = $555/month. Per-query: $0.00055. Breakeven vs. GPT-4o-mini at $0.0003/query: need ~3M queries/month to break even. At 1M queries, API is 2x cheaper.

Częste błędy do unikania

✕Forgetting to include engineering time for model serving setup, monitoring, and maintenance (10-40 hours/month)
✕Not accounting for GPU memory requirements — a 70B model needs at minimum 140GB GPU RAM (2× A100 80GB or quantized)
✕Comparing self-hosted open-source model costs against API costs without normalizing for output quality differences

Często zadawane pytania

When does self-hosting an LLM become cheaper than API services?

For frontier-equivalent quality (70B+ models), self-hosting typically breaks even at 500K-2M queries per month compared to GPT-4o pricing. For smaller models competing with GPT-4o-mini, the breakeven is much higher (3M+ queries/month) because mini model API pricing is already very low. Data privacy and customization needs may justify self-hosting even at lower volumes.

Can I run a 70B model on a single GPU?

Not at full precision. A 70B parameter model requires ~140GB GPU RAM at FP16. Using 4-bit quantization (GPTQ or AWQ), it fits on a single 80GB A100 or H100 with acceptable quality loss. For production serving with good throughput, 2× A100 80GB or 1× H100 is recommended.

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