How Much Does It Cost to Run a 70B Parameter Model?

Hardware Requirements for 70B Models

Running a 70B parameter model like LLaMA 3 70B, Qwen 2.5 72B, or Mistral Large requires serious GPU memory. At FP16 precision, a 70B model needs approximately 140GB of VRAM just for model weights, plus additional memory for KV cache and inference overhead. That means you need multiple GPUs. Here is what dedicated GPU server hosting actually costs for a 70B model.

The good news: with quantisation (reducing precision from FP16 to INT4 or INT8), you can fit a 70B model on fewer GPUs while maintaining strong quality. Our VRAM optimisation guide covers quantisation tradeoffs in detail.

GPU Options and Monthly Costs

The sweet spot for most teams is 2x RTX 6000 Pro 96 GB at $599/month. It handles 70B models at full FP16 precision with room for KV cache, delivering 40-65 tokens per second. For higher throughput, a multi-GPU cluster with 4x RTX 6000 Pros doubles your capacity. Verify numbers with our tokens per second benchmarks.

Cost per Token by GPU

This is where self-hosting shines. The cost per token depends entirely on utilisation. The more you use your server, the cheaper each token becomes:

Max tokens/month assumes 24/7 operation with continuous batching via vLLM. Actual throughput varies with sequence length and batch size.

For complete per-GPU breakdowns across specific models, see our cost-per-1M-token guides for LLaMA 3, DeepSeek, and Mistral. Our cost per million tokens calculator covers all models.

70B Self-Hosted vs API Pricing

How does running your own 70B model compare to equivalent API pricing? Here is the comparison using a 2x RTX 6000 Pro setup at $599/month:

At 100M tokens, self-hosting breaks even with most premium APIs. At 500M+ tokens, self-hosting saves $1,500-$3,000 per month. The break-even analysis depends on which API you are replacing.

Quantisation: Trading Quality for Cost

Quantisation reduces model precision to fit on fewer GPUs. The tradeoffs:

INT8 quantisation offers the best quality-to-cost ratio: near-identical quality on a single RTX 6000 Pro at $299/month instead of $599 for dual RTX 6000 Pros. Learn more in our best GPU for LLM inference guide.

Multi-GPU Scaling Economics

For teams needing higher throughput, multi-GPU clusters provide linear scaling:

• 2x RTX 6000 Pro ($599/mo): 40-65 tok/s, ideal for most production workloads
• 4x RTX 6000 Pro ($899/mo): 80-120 tok/s, handles high-concurrency applications
• 8x RTX 6000 Pro ($1,599/mo): 150-200 tok/s, enterprise-grade throughput

Even at 8x RTX 6000 Pro scale, the cost is $1,599/month with unlimited tokens. That same throughput on premium APIs would cost $10,000-$50,000+ per month. See how this fits into broader GPU hosting ROI calculations.

The Bottom Line

Running a 70B parameter model costs between $149/month (quantised, single GPU) and $599/month (full precision, dual RTX 6000 Pro). At moderate to high volume, this is dramatically cheaper than any commercial API offering equivalent quality. Choose the cheapest GPU that meets your throughput and quality requirements, and explore open-source LLM hosting options to get started.