--- title: "The tokenomics of self-hosted LLMs" url: https://daily.dev/posts/the-tokenomics-of-self-hosted-llms-rmgohnaqs source_url: https://developers.redhat.com/articles/2026/08/19/tokenomics-self-hosted-llms type: article source: "Red Hat Developer" published: 2026-08-19T07:31:30.342Z updated: 2026-08-19T07:31:56.812Z tags: ["gpu", "finops", "mlops", "ai-inference", "vllm"] reading_time: 12 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The tokenomics of self-hosted LLMs **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 12 min read · 0 upvotes · 0 comments ## Summary Self-hosted LLM economics come down to two levers: reducing operating cost and increasing tokens actually served. Cost per token equals total operating cost (hardware, software, people, facilities) divided by tokens processed, and the piece walks through right-sizing GPU instances, autoscaling replicas, consolidating workloads to fill idle GPU cycles, and measuring real-world throughput versus theoretical maximum with tools like GuideLLM. It also covers how model size and quantization (e.g., FP8) affect hardware requirements, how input, cached, and output tokens carry different effective GPU costs, and how vLLM and llm-d features like KV cache offloading, intelligent routing, and speculative decoding can improve throughput and effective cost per token. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do you calculate the actual cost per token for a self-hosted LLM deployment? Cost per token equals total operating cost divided by tokens actually processed over the same period, not the theoretical maximum throughput. For example, $50,000 in monthly infrastructure cost divided by 500 million tokens processed works out to about 10 cents per million tokens. Reducing spend or increasing real usage both lower this figure. _daily.dev surfaces practical breakdowns like this for engineers sizing GPU budgets against real token volume._ ### Why are output tokens more expensive to generate than input tokens on a GPU-hosted LLM? Output tokens are generated sequentially, one at a time, which is far less parallelizable than processing input tokens in a single pass. Input tokens benefit from continuous batching and are relatively efficient, while cached tokens reused from the KV cache are cheap since they're looked up rather than recomputed, making output-heavy workloads the priciest per token. _Understanding GPU token economics helps teams budgeting inference costs; daily.dev tracks these serving optimization techniques._ ### How much smaller GPU hardware do you need to serve Llama-3.1-8B-Instruct versus Llama-3.3-70B-Instruct? Llama-3.1-8B-Instruct fits on a single H100 or even a budget GPU like an L40S, while Llama-3.3-70B-Instruct can require up to four H100s per instance. Choosing the smaller model when quality is sufficient cuts hardware cost sharply and increases achievable throughput per dollar. _Comparing model sizes against GPU budgets is a recurring decision daily.dev helps engineers stay informed on._ ## Similar posts on daily.dev - [The LLM Inference Trilemma: Throughput, Latency, Cost](https://daily.dev/posts/the-llm-inference-trilemma-throughput-latency-cost-ws3duzsii) · DigitalOcean · 0 upvotes · 0 comments - [Self-Hosting Your First LLM](https://daily.dev/posts/self-hosting-your-first-llm-13jpheaxf) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#gpu](https://daily.dev/tags/gpu), [#finops](https://daily.dev/tags/finops), [#mlops](https://daily.dev/tags/mlops), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/the-tokenomics-of-self-hosted-llms-rmgohnaqs)