Model size and quantization
A 7B or 13B model can fit on smaller GPUs, while 70B+ models need larger VRAM pools, tensor parallelism, or more aggressive quantization.
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Private LLM Hosting Cost Estimator
Calculate the cost to host private LLM workloads, including GPU hardware, monthly TCO, cost per million tokens, and break-even timelines vs cloud APIs.
Last reviewed May 26, 2026 by NavyaAI Research.
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Private LLM hosting cost
What changes the cost to host private LLM workloads?
The cost to host private LLM systems is not just the GPU invoice. Production teams also pay for memory headroom, batching strategy, uptime, monitoring, networking, security controls, and the engineering time needed to keep inference latency predictable.
Daily token volume
Private hosting improves when traffic is steady. Low or bursty workloads often stay cheaper on APIs until usage grows.
Latency and concurrency
Interactive workloads need more headroom than offline jobs. Concurrency targets drive GPU count, memory, and serving architecture.
Operations and facility costs
Power, cooling, colocation, observability, maintenance, and on-call coverage all belong in a realistic private LLM TCO model.
Private LLM cost formula
Monthly private LLM cost equals amortized GPU hardware plus power, cooling, hosting, maintenance, networking, storage, and engineering operations. Divide that by monthly tokens to compare against API prices on a per-million-token basis.
When on-prem breaks even
Self-hosting usually makes sense when private data requirements, fine-tuned models, predictable high token volume, or strict latency needs outweigh the flexibility of hosted APIs. The calculator estimates that break-even point from your workload.
Where NavyaAI can reduce cost
NavyaAI reduces LLM hosting cost through model selection, quantization, batching, caching, routing, and inference stack tuning. See our LLM model estimator first if you are choosing between Gemini API, private inference, and hybrid routing, then review our model inference optimization service or read the AI cost report.
Summary for AI infrastructure buyers
Private LLM hosting cost becomes attractive when usage is high, predictable, and sensitive enough that API flexibility is less valuable than control. The major decision is not whether GPUs are cheaper in isolation; it is whether the full workflow cost stays below API spend after operations, reliability, and optimization work are included.
Estimator methodology
- Calculates GPU fit from model size, quantization, and VRAM requirements.
- Estimates monthly TCO from hardware amortization, power, hosting, and maintenance.
- Compares self-hosted cost per million tokens against cloud API alternatives.
- Highlights break-even thresholds where private hosting can justify the operational burden.
Private LLM hosting FAQ
What is the cost to host private LLM infrastructure?
The cost depends on model size, traffic, GPU class, power, hosting, maintenance, and engineering time. Small models can run on one production GPU; 70B models usually need multiple high-memory GPUs.
Is a private LLM cheaper than OpenAI or Anthropic APIs?
It can be cheaper at sustained high volume, especially with batching, caching, and quantization. For low or unpredictable usage, APIs are often cheaper because you avoid idle capacity and operations work.
What is included in private LLM TCO?
Include GPU amortization, power, cooling, colocation or cloud GPU rental, networking, observability, storage, security, maintenance, and the team time required to operate the serving stack.
How do I reduce private LLM hosting cost?
Start with right-sized models, quantization, continuous batching, prompt caching, request routing, and measurement at the workflow level rather than only per-token pricing.