TL;DR: Running Llama 70B locally costs roughly $0.10-0.18 per 1,000 tokens on mid-range hardware ($8,000-$15,000), compared to $0.40-0.80 on cloud GPUs. The break-even point is around 3-6 months of consistent daily use (4+ hours). But the upfront capital, quantization trade-offs, and operational complexity mean cloud still wins for variable or experimental workloads.
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The Question Everyone Asks
Every week, someone asks: "Should I buy hardware or just use cloud GPUs?"
It's a reasonable question. H100s on RunPod run $2.39/hour. A capable workstation costs $8,000-$15,000 upfront. At what point does owning hardware make financial sense?
I decided to run the numbers. Not vague hand-waving about "it depends" - actual calculations with real hardware prices, published benchmarks, and transparent assumptions.
Important Disclaimer: What This Analysis Is (and Isn't)
I don't have millions of dollars to test-drive enterprise AI hardware. What I do have is pricing data from 872 products across 25 vendors, published benchmarks from reputable sources, and a calculator.
The math in this analysis is based on:
- Hardware pricing: AI Hardware Index catalog (current as of December 2025)
- Performance benchmarks: Published data from GPU-Benchmarks-on-LLM-Inference, Lambda Labs, and vendor specifications
- Cloud pricing: Public rates from RunPod and Lambda Labs
- Power costs: US average electricity rates from the EIA
I haven't personally benchmarked every configuration. Your actual performance will vary based on software optimization, batch sizes, context length, and a dozen other factors. The numbers here are directionally correct, but treat them as estimates to inform your decision - not guarantees.
The Hardware Configurations
I'm comparing three tiers of local hardware, all capable of running Llama 70B:
| Tier | Configuration | Total Cost | VRAM | Quantization Required |
|---|---|---|---|---|
| Entry | Bizon V3000 G4 (1x RTX 5090) | $3,200 | 32GB | 4-bit (INT4) |
| Mid | Bizon ZX4000 (2x RTX 5090) | $13,600 | 64GB | 8-bit (INT8) |
| High | H100 PCIe + Workstation | ~$40,000 | 80GB | Full precision (FP16) |
Why These Configurations?
Llama 70B has specific memory requirements:
- FP16 (full precision): ~140GB VRAM
- INT8 (8-bit quantization): ~70GB VRAM
- INT4 (4-bit quantization): ~35-42GB VRAM
A single RTX 5090 (32GB) can run 70B at 4-bit quantization. Two RTX 5090s (64GB combined) handle 8-bit comfortably. Full precision requires an H100 80GB or multi-GPU configurations with high-bandwidth interconnects.
The Performance Numbers
Published benchmarks show significant variation based on quantization level and hardware:
| Configuration | Quantization | Tokens/Second | Source |
|---|---|---|---|
| 1x RTX 5090 | Q4KM | ~50-60 tok/s | Bizon Tech benchmarks |
| 2x RTX 4090 | Q4KM | ~40-50 tok/s | GPU-Benchmarks-on-LLM-Inference |
| 1x H100 PCIe | FP16 | ~20-25 tok/s | DatabaseMart H100 benchmarks |
| 1x H100 PCIe | Q4KM | ~80-100 tok/s | Estimated from FP16 scaling |
Important caveat: These numbers come from various sources with different software stacks, batch sizes, and testing methodologies. Real-world performance depends heavily on your specific setup. The RTX 5090 numbers are particularly new - early benchmarks suggest roughly 35% improvement over RTX 4090 for LLM inference, but the software ecosystem is still maturing.
The Cost Calculation
Hardware Costs (Amortized Over 3 Years)
Assuming 3-year useful life with 20% residual value:
| Tier | Hardware Cost | 3-Year Depreciation | Monthly Hardware Cost |
|---|---|---|---|
| Entry | $3,200 | $2,560 | $71 |
| Mid | $13,600 | $10,880 | $302 |
| High | $40,000 | $32,000 | $889 |
Power Costs
Using published TDP values and US average electricity ($0.12/kWh):
| Configuration | System Power | Monthly (8hr/day) | Monthly (24/7) |
|---|---|---|---|
| 1x RTX 5090 | ~700W | $20 | $60 |
| 2x RTX 5090 | ~1,200W | $35 | $104 |
| H100 PCIe system | ~800W | $23 | $69 |
Note: These are estimates. Actual power draw during inference is typically 60-80% of TDP. System overhead (CPU, RAM, storage) adds roughly 200-300W.
Total Monthly Operating Cost
For 8 hours/day usage (a reasonable development/production workload):
| Tier | Hardware (amortized) | Power | Total Monthly |
|---|---|---|---|
| Entry | $71 | $20 | $91 |
| Mid | $302 | $35 | $337 |
| High | $889 | $23 | $912 |
Cost Per Token Analysis
Here's where it gets interesting. Let's calculate cost per 1,000 tokens for each tier.
Assumptions:
- 8 hours of active inference per day
- 22 working days per month
- Performance numbers from benchmarks above
| Tier | Tok/s | Tokens/Month (8hr/day) | Cost/Month | Cost per 1K Tokens |
|---|---|---|---|---|
| Entry | 55 | 34.8M | $91 | $0.0026 |
| Mid | 50 | 31.7M | $337 | $0.011 |
| High | 90 | 57.0M | $912 | $0.016 |
Wait, the Entry Tier Has the Lowest Cost Per Token?
Yes - if you're comparing pure inference throughput. The single RTX 5090 with aggressive quantization delivers surprisingly good tokens-per-dollar.
But there's a catch: quantization affects quality. The INT4 model running on Entry tier isn't producing the same outputs as FP16 on the H100. For many use cases (chatbots, summarization, code completion), the difference is negligible. For tasks requiring maximum precision (medical, legal, financial analysis), it matters.
Cloud Comparison
Let's compare to cloud GPU pricing (as of December 2025):
| Provider | GPU | Hourly Rate | Tok/s (70B) | Cost per 1K Tokens |
|---|---|---|---|---|
| RunPod | H100 PCIe 80GB | $2.39 | ~90 | $0.0074 |
| Lambda Labs | H100 SXM | $2.49-$3.29 | ~100 | $0.0069-0.0091 |
| RunPod | A100 80GB | $1.19 | ~40 | $0.0083 |
Cloud advantages:
- No upfront capital
- Pay only for what you use
- Easy to scale up/down
- No maintenance or power infrastructure
Cloud disadvantages:
- Ongoing costs that never end
- Data leaves your premises
- Availability can be inconsistent
- Network latency for API calls
The Break-Even Analysis
At what usage level does owning hardware beat renting cloud?
Monthly cloud cost for equivalent performance:
- Entry tier equivalent (50 tok/s): ~$200/month at 8hr/day on A100
- Mid tier equivalent (50 tok/s): ~$200/month at 8hr/day on A100
- High tier equivalent (90 tok/s): ~$425/month at 8hr/day on H100
Break-even timeline:
| Tier | Hardware Cost | Cloud Equivalent | Break-Even |
|---|---|---|---|
| Entry ($3,200) | $91/month | ~$200/month | ~29 months |
| Mid ($13,600) | $337/month | ~$200/month | Never (cloud cheaper) |
| High ($40,000) | $912/month | ~$425/month | Never (cloud cheaper) |
This is the uncomfortable truth: For pure cost optimization, the math often favors cloud - especially for mid and high-tier configurations. The Entry tier breaks even eventually, but you're accepting quantization trade-offs.
When Local Hardware Makes Sense Anyway
Cost isn't everything. Here's when buying hardware is the right call despite the numbers:
1. Data Privacy and Security
If your data can't leave your premises (healthcare, finance, legal, government), cloud isn't an option. The "cost" of a data breach or compliance violation dwarfs any hardware expense.
2. Consistent, Predictable Workloads
If you're running inference 24/7 for production applications, the math shifts. At 24/7 operation:
| Tier | Local Monthly | Cloud Monthly | Break-Even |
|---|---|---|---|
| Entry | $131 | ~$600 | ~7 months |
| Mid | $406 | ~$600 | ~33 months |
| High | $958 | ~$1,275 | ~126 months |
Heavy, consistent usage dramatically improves the local hardware economics.
3. Iteration Speed for Development
No network latency. No API rate limits. No waiting for instance availability. For rapid prototyping and development, local hardware removes friction that costs developer time - which is often more expensive than compute.
4. Learning and Experimentation
You can't truly understand how these systems work without hands-on experience. A $3,200 workstation is an investment in knowledge that pays dividends beyond the immediate cost analysis.
The Hidden Costs Nobody Mentions
Local Hardware
- Time: Setup, maintenance, troubleshooting, driver updates
- Space: Workstations are loud and hot; you need appropriate space
- Cooling: May need upgraded HVAC, especially for multi-GPU
- Risk: Hardware failure means downtime; no redundancy unless you buy two
- Opportunity cost: Capital tied up in depreciating equipment
Cloud
- Egress fees: Some providers charge for data transfer
- Storage costs: Model weights, checkpoints, datasets add up
- Spot instance risk: Cheaper rates but can be interrupted
- Vendor lock-in: Workflows built around specific cloud APIs
My Recommendations
Buy Local Hardware If:
- Data privacy is a hard requirement
- You have consistent, heavy usage (8+ hours/day, most days)
- You're comfortable with 4-bit quantization trade-offs
- You want to learn how this stuff actually works
- You have appropriate space, power, and cooling
Recommended starting point: Bizon V3000 G4 or similar RTX 5090 workstation (~$3,200). Best cost-per-token, capable of running 70B models at usable speeds, and a reasonable entry point to learn.
Stick With Cloud If:
- Workloads are variable or experimental
- You need full-precision inference quality
- Capital is constrained but operating budget is available
- You don't want to manage hardware
- You need to scale beyond a single machine
Recommended providers: RunPod for cost-effectiveness, Lambda Labs for reliability. Both offer H100s under $3/hour with per-second billing.
Appendix: Calculation Details
Depreciation Model
- Useful life: 3 years (conservative for GPU hardware)
- Residual value: 20% (GPUs hold value reasonably well)
- Monthly depreciation = (Purchase Price × 80%) / 36 months
Power Calculation
- Electricity rate: $0.12/kWh (US average)
- GPU power: TDP values from manufacturer specs
- System overhead: +250W (CPU, RAM, storage, cooling)
- Actual draw during inference: ~70% of TDP (estimated)
Token Throughput
- Tokens per month = (tokens/second) × 3600 × (hours/day) × 22 days
- Cost per 1K tokens = Monthly cost / (Tokens per month / 1000)
Cloud Equivalent Calculation
- Monthly cloud cost = Hourly rate × Hours/day × 22 days
- Matched to approximate local throughput capability
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Analysis based on AI Hardware Index catalog data and published benchmarks. Prices and performance figures current as of December 2025. Cloud pricing changes frequently—verify current rates before making decisions.