The true cost of local LLM inference
Local deployment using an RTX 4090 costs approximately $104 per month, allowing high-volume users to break even against GPT-4o costs in under three months. This analysis compares electricity and hardware amortization against Groq and OpenAI API pricing.
I measured the marginal energy cost of generating tokens locally using a 256-token generation loop. The experiment used an openSUSE machine with a single RTX 3090.
The cost hit €1.526 per million tokens.
The DeepSeek-R1-Distill 32.8B model draws 155W of power, which is less than the 283W drawn by gemma3:27b. Even with lower power draw, it remains the most expensive model on the list. This model spends much of its time deliberating between generations rather than producing text. This reasoning leads to an effective delivery rate of 3.7 tokens per second. That rate is the lowest of any model tested. In contrast, gemma3:1b hits 136 tokens per second while drawing 154W. Devstral pulls 320W of power during its run. GLM-4.5-Air, with 106B parameters, costs €1.040 per million tokens. The Bulgarian electricity rate of 0.30 BGN per day was used to calculate these figures.
Local deployment costs include hardware amortization and electricity. An RTX 4090 at $1,600 amortized over 24 months costs $66.67 per month. Electricity for an RTX 4090 drawing 400W at $0.13 per kWh costs $12.48 per month for eight hours of daily use. A developer using an Apple Silicon Mac drawing 75W pays $7.02 per month for 24/7 operation. The math shifts.
Speed versus token rates
Groq provides LPU-powered speed. Groq’s LPU hardware achieves a time-to-first-token of 120ms for Llama 3.3 70B, which beats OpenAI’s GPT-4o average of 450ms for similar workloads. You know that latency drives user experience. Groq’s LPU eliminates branch prediction and caches to focus on inference. This architecture delivers output speeds three to 10 times faster than GPU-based alternatives. However, Groq lacks the model variety of Together AI. Together AI carries over 100 models. NVIDIA signed a $20 billion licensing agreement for Groq’s technology in late 2025.
| Provider | Model | Input ($/1M) | Output ($/1M) |
|---|---|---|---|
| OpenAI | GPT-5.6-sol | $2.00 | $10.00 |
| OpenAI | GPT-4o-mini | $0.15 | $0.60 |
| Groq | Llama 3.3 70B | $0.59 | $0.79 |
| Groq | Llama 3.1 8B | $0.05 | $0.08 |
Hardware matters most. Groq’s Llama 3.1 8B costs $0.05 for input and $0.08 for output. OpenAI’s GPT-4o-mini costs $0.15 for input and $0.60 for output. Together AI offers Llama 3.3 70B output at $0.54 per million tokens. For a developer generating 50 million tokens per month against GPT-4o with a blended rate of $13 per million, an RTX 4090 with a monthly cost of $104 breaks even in under three months.
When to switch to local
I recommend local hosting for high-volume batch processing. The choice depends on workload types. A coding assistant using 50 million tokens a month against GPT-4o costs $650. An RTX 4090 at $104 per month breaks even in under three months. However, Ollama lacks official function calling support. This limitation forces developers to implement external orchestration for structured tool use. Ollama also scales only to the capacity of the self-hosted server cluster and caps at four parallel requests.
RAG pipelines with 300 million tokens per month on Claude 3 Haiku cost $315. An RTX 4090 running a 7B model at $104 per month breaks even in month two. Small models reach a break-even point within a few months, but larger models take five years. Organizations with high-volume processing of over 50 million tokens per month find local deployment viable. It costs more. Will small startups ever abandon the convenience of managed APIs?
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