Cheapest Way to Run LLMs: GPU Rental vs API Costs Compared
2026-06-01 · 8 min read
The Core Tradeoff: Fixed Cost vs Variable Cost
Running a large language model in production always comes down to the same decision: pay a fixed hourly rate for dedicated GPU capacity, or pay variable per-token API fees. Neither is universally cheaper — the answer depends entirely on your traffic volume and latency requirements.
This guide breaks down the math with real pricing data so you can make the right call for your workload.
API Pricing: Who Offers What
The LLM API market has fragmented into two tiers:
Official Model Providers
OpenAI, Anthropic, Google, and DeepSeek offer their proprietary models directly. These are the canonical sources for GPT-4o, Claude, Gemini, and DeepSeek pricing. You pay for access to models you can't run yourself.
Open-Model API Platforms
Groq, Together AI, Fireworks AI, SambaNova, and HuggingFace all offer hosted inference for open-weight models like Llama 3.3, DeepSeek R1, and Qwen2.5. Prices vary significantly for identical models:
| Model | Cheapest API ($/1M tok) | Most Expensive | Spread |
|---|---|---|---|
| Llama 3.3 70B | $0.59 (Groq) | $0.90 (HuggingFace) | 53% |
| DeepSeek R1 | $0.55 (DeepSeek official) | $8.00 (Fireworks) | 14× |
| Qwen2.5 72B | $0.60 (SambaNova) | $0.90 (HuggingFace) | 50% |
Prices from ComputeUnion, June 2026. Input + output averaged.
GPU Rental: The Self-Hosting Math
When you rent a GPU, you pay a fixed rate regardless of whether you're generating tokens or sitting idle. The key metric is your GPU utilization rate — the fraction of time the GPU is actually running inference.
Throughput Benchmarks (tokens/hr)
| Model Size | GPU | Tokens/hr (est.) |
|---|---|---|
| 8B (Llama 3.1 8B) | RTX 4090 | 3–5M |
| 70B (Llama 3.3 70B) | H100 80GB | 500K–1M |
| 70B (Llama 3.3 70B) | 2× A100 80GB | 400–800K |
| 405B (Llama 3.1 405B) | 8× H100 SXM | 200–400K |
Break-Even Calculations
At Groq's Llama 3.3 70B price of ~$0.69/1M tokens and an H100 at $2.50/hr:
Break-even = $2.50 / $0.00000069 = 3.6M tokens/hr
Since an H100 tops out at ~750K tokens/hr for 70B models, self-hosting a 70B model on an H100 is never cheaper than Groq at current prices — unless you're running a cluster of 5+ H100s at full utilization.
For a 7B/8B model on RTX 4090 ($0.74/hr on budget platforms) vs. API at $0.18/1M tokens:
Break-even = $0.74 / $0.00000018 = 4.1M tokens/hr
An RTX 4090 can hit 3–5M tokens/hr for 8B models. At full utilization, self-hosting is roughly break-even with HuggingFace's API — and cheaper if you drive the GPU hard.
When to Use API Pricing
- Bursty or unpredictable traffic — API scales to zero cost when idle; GPU keeps billing
- Low-to-medium volume — Under ~1M tokens/day, API is almost always cheaper
- Proprietary models — GPT-4o, Claude 3.5, Gemini 2.5 aren't self-hostable
- Fast prototyping — No infrastructure overhead; start in minutes
When to Rent a GPU
- High, sustained traffic — >5M tokens/day for large models makes GPU competitive
- Data privacy — On-premise or single-tenant GPU keeps data off third-party APIs
- Custom fine-tuned models — You can't deploy your LoRA adapter on Groq
- Lowest-latency requirements — Dedicated GPU avoids shared API throttling
The Hybrid Approach
Many production systems combine both: use a cheap GPU for high-traffic predictable workloads (e.g., batch summarization), fall back to API for spikes or model variants that aren't worth self-hosting. This gives cost efficiency without sacrificing availability.
Finding the Best Current Prices
ComputeUnion's LLM pricing page shows official vs. relay prices side by side for 100+ models, updated every 6 hours. The GPU comparison page tracks hourly rates across 20+ cloud platforms. Use both to keep your cost model current — prices in this market move fast.