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Total Cost of Ownership for Self-Hosted LLMs
The total cost of ownership (TCO) of a self-hosted LLM is everything it costs to run an open-weight model on your own or rented hardware.
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Résumé
That includes GPUs, power, networking, software, engineering staff, idle capacity and upgrades, not just the price of the GPUs. Comparing that total with per-token API pricing shows whether self-hosting actually saves money. The answer usually depends on keeping the hardware busy most of the time.
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Self-hosting means downloading an open-weight model, such as a Llama, Mistral, Qwen or Gemma variant, and serving it on infrastructure you control. That can be servers you own or GPUs you rent in the cloud. The weights may be free to download, but running them is not. Total cost of ownership has several layers: - **Hardware.** Purchased GPUs are depreciated over a few years. Rented GPUs are billed by the hour whether they are busy or idle. Owned hardware also needs power, cooling, rack space and networking. - **Software and operations.** You need an inference server such as vLLM, SGLang or NVIDIA TensorRT-LLM. You also need load balancing, monitoring, logging, security hardening, and redundancy so one failed node does not take the service down. - **Staff.** This is often the largest hidden cost: engineers who deploy, tune, patch and upgrade the stack, plus someone on call. - **Upgrades.** New models arrive often. Each migration means re-evaluating, re-tuning and sometimes buying new hardware. Utilization decides the outcome. An API charges only for the tokens you use. A self-hosted GPU costs the same whether it serves one request or thousands. Effective cost per token is total monthly cost divided by tokens actually served. A lightly used cluster can cost far more per token than an API, while a steadily loaded one can cost much less. Three misconceptions are common. The first is that GPU rental price times hours equals the cost, which ignores staff and idle time. The second is that open weights mean free. The third is that self-hosting is automatically more private. A poorly secured server can be riskier than a provider with audited controls. There are good reasons to self-host: - control over your data - predictable costs at high, steady volume - customization through fine-tuning - freedom from a provider changing or retiring a model
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The Future of Total Cost of Ownership for Self-Hosted LLMs
The self-hosting calculation keeps moving in both directions. Open-weight models have become more capable, and quantization and efficient serving software let smaller hardware handle useful workloads. At the same time, API prices have fallen, and managed services increasingly host open models for a per-token fee, which removes much of the operations burden. For many organizations the likely pattern is hybrid: APIs for spiky or frontier workloads, and self-hosting for steady, high-volume or sensitive ones. Treat the decision as something to review regularly, not a one-time choice. Hardware generations, model releases and prices all change within a typical depreciation period.
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A company rents GPUs to serve a 70-billion-parameter model for an internal assistant. Traffic comes only during office hours, so the GPUs sit idle most nights and each token ends up costing more than the API it replaced.
A bank whose internal policy requires sensitive data to stay on infrastructure it controls self-hosts a mid-size open model in its own data center. It accepts a higher cost in exchange for that control.
A document-classification pipeline that runs around the clock serves a small quantized model on a single GPU with continuous batching. Its cost per token ends up well below API rates.
A startup budgets only for cloud GPU rental. It later finds it needs an engineer on call to handle serving outages, monitoring, security patches and model upgrades, and that staff cost exceeds the hardware bill.
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What is Total Cost of Ownership for Self-Hosted LLMs?
The total cost of ownership (TCO) of a self-hosted LLM is everything it costs to run an open-weight model on your own or rented hardware. That includes GPUs, power, networking, software, engineering staff, idle capacity and upgrades, not just the price of the GPUs. Comparing that total with per-token API pricing shows whether self-hosting actually saves money. The answer usually depends on keeping the hardware busy most of the time.
Roughly how much memory do the weights of a 70-billion-parameter model need at 16-bit precision?
At 16 bits (2 bytes) per parameter, 70 billion × 2 bytes is about 140 GB. That is more than a single 80 GB GPU, before any KV cache memory is added.
How does the guide define the effective cost per token of a self-hosted deployment?
Dividing all monthly costs, including staff and idle capacity, by the tokens actually served gives a figure you can compare directly with API per-token prices.
Why can a lightly used self-hosted cluster cost more per token than an API?
An API bills only for use, but self-hosted hardware and staff cost the same regardless of load. Low utilization spreads those fixed costs over fewer tokens.
Which cost does the guide identify as often the largest hidden line in self-hosting?
Budgets often count only hardware. Running a reliable inference service takes engineering time, and that labor can exceed the GPU bill.
Besides the model weights, what uses GPU memory and grows with context length and the number of concurrent requests?
The KV cache stores attention keys and values for active requests. Longer contexts and more simultaneous users need more of it.
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