When building LLM inference machines, the most fundamental question is: consumer hardware or enterprise hardware? After extensive research and real-world testing, here's my comprehensive analysis comparing 4x RTX 3090s to 2x A100 80GBs.
The Hardware Showdown
Option 1: Consumer RTX 3090 Build
| Component | Est. Cost | Notes |
|---|---|---|
| Supermicro M12SWA-TF Motherboard | $900 | 6x PCIe 4.0 x16, workstation grade |
| Threadripper Pro 5955WX | $1,300 | 64C/128T, 128 PCIe lanes |
| 256GB DDR4-3200 ECC RDIMM | $500 | 8-channel configuration |
| 4x RTX 3090 (2 existing + 2 new) | $2,800 | 96GB VRAM total, ~$700 each used |
| 1600W PSU + Power Adapter | $350 | Dual PSU setup for headroom |
| Cooling + Frame + Risers | $350 | Open-air with quality risers |
| Total | $6,200 | ~$5,000-5,400 realistic |
Option 2: Enterprise A100 Server
| Component | Est. Cost | Notes |
|---|---|---|
| 2x A100 80GB PCIe | $20,000-30,000 | New pricing, used $15,000-20,000 |
| Server Chassis + CPU + RAM | $5,000-10,000 | Enterprise-grade server components |
| Total | $25,000-40,000 | ~$25,000 realistic (used) |
The Price Impact
5x more expensive for enterprise hardware
Head-to-Head Performance Comparison
| Metric | 4x RTX 3090 | 2x A100 80GB | Winner |
|---|---|---|---|
| Total VRAM | 96GB | 160GB | A100 |
| Memory Bandwidth | ~3.7 TB/s | ~4 TB/s | A100 (slight) |
| Memory Type | GDDR6X | HBM2e (lower latency) | A100 |
| FP16 TFLOPS | ~140 | ~156 | A100 (10% edge) |
| Power Draw | ~1,400W | ~600W | A100 |
| NVLink Support | No | Yes (600 GB/s) | A100 |
| 24/7 Rated | No | Yes | A100 |
| Noise Level | Loud | Loud | Tie |
| Total Cost | ~$5,000 | ~$25,000 | RTX 3090 |
| Cost per GB VRAM | ~$52/GB | ~$156/GB | RTX 3090 |
Understanding the Technical Differences
Memory Architecture: GDDR6X vs HBM2e
RTX 3090 Advantages
- Higher Clock Speed: GDDR6X runs at ~19.5 Gbps vs A100's ~1.6 Gbps
- Lower Upfront Cost: Consumer pricing with abundant used market
- Incremental Expansion: Add GPUs as budget allows
- Easy Maintenance: Standard PC components, widely available
A100 Technical Superiority
- HBM2e Lower Latency: Memory much closer to GPU cores
- NVLink Interconnect: 600 GB/s unified memory pool vs PCIe bottleneck
- ECC Memory: Built-in error correction for reliability
- Datacenter Grade: Designed for 24/7 operation under heavy load
PCIe Bandwidth Realities
RTX 3090 Multi-GPU Performance
- 6x PCIe 4.0 x16 slots = 96 GB/s total bandwidth
- No cross-GPU memory sharing (each GPU works independently)
- Model splitting required for large models (llama.cpp splits by layer)
A100 NVLink Advantage
- NVLink provides 600 GB/s between GPUs (much faster than PCIe)
- Memory sharing enables larger effective context windows
Practical Performance Analysis
Real-World LLM Inference
Why RTX 3090 Often Wins for Home Setups
- Cost Efficiency: 5x less expensive for 60% of the VRAM
- Flexibility: Can upgrade incrementally as needs grow
- Adequate Performance: Still runs 70B models at Q4 quantization
- Easier Entry Point: Start with 1-2 GPUs, expand later
When A100 Makes Financial Sense
Enterprise-Only Requirements
- Production Services: Need 99.99% uptime and 24/7 reliability
- Rack Density: Space-constrained datacenter deployments
- Power Constraints: 600W vs 1,400W significant for datacenter costs
- SaaS Applications: Business model can absorb hardware costs
The Math: Cost Performance Per Dollar
Raw TFLOPS Analysis
| Configuration | Total FP16 TFLOPS | Total Cost | TFLOPS per $1000 |
|---|---|---|---|
| 4x RTX 3090 | ~140 TFLOPS | $5,000 | 28 TFLOPS |
| 2x A100 80GB | ~156 TFLOPS | $25,000 | 6.2 TFLOPS |
Performance Per Dollar
4.5x more performance per dollar with RTX 3090s
VRAM Cost Analysis
| Configuration | VRAM Capacity | Cost per GB | Models Supportable |
|---|---|---|---|
| 4x RTX 3090 | 96GB | $52/GB | 70B Q4, 100B+ MoE models |
| 2x A100 80GB | 160GB | $156/GB | 100B+ Q8, very large context |
Real-World Decision Factors
Choose RTX 3090 Setup When:
- Budget constraints: $5K vs $25K is a massive difference
- Learning/Development: Consumer hardware more forgiving to experiment with
- Incremental Growth: Start small, expand as needs justify
- Hobbyist/Prosumer Use: Not running 24/7 production workloads
- DIY Preference: Want to understand and control every aspect of your setup
Choose A100 Setup When:
- Business Investment: Revenue can justify 5x higher cost
- Production Service: 99.99% uptime and enterprise reliability requirements
- Datacenter Space: Need maximum compute per rack unit
- Power Efficiency: Higher electricity costs make 600W vs 1,400W significant
- Unified Memory Needs: Single memory pool above 100GB is essential
Hidden Costs and Considerations
RTX 3090 Additional Expenses
- Higher Electricity Bills: ~1,400W vs ~600W = ~$200-300/month difference
- Noise Management: Open-air setups require acoustic considerations
- Cooling Infrastructure: May need room modifications or A/C upgrades
- Maintenance Overhead: Consumer components may need more frequent replacement
A100 Hidden Advantages
- Lower TCO: Longer lifespan, warranty, support contracts
- Datacenter Integration: Standardized form factors, enterprise management tools
- Software Ecosystem: Enterprise drivers, validation, and optimization
- Resale Value: Better retention of value despite higher initial cost
My Recommendation: RTX 3090 for Most Cases
Bottom Line
For learning, development, and even serious production workloads, the 4x RTX 3090 setup delivers 4.5x better performance per dollar. The cost savings ($20K) can be invested in better cooling, backup systems, or simply saved.
The Sweet Spot
The consumer setup hits the sweet spot where VRAM capacity (96GB) is sufficient for most large models while maintaining reasonable costs. Even at full retail, you're getting enterprise-class performance at consumer prices.
Exception Cases
The only scenarios where A100 makes sense are when you need:
- Uninterrupted 24/7 production service
- Datacenter rack density efficiency
- NVLink benefits for very large workloads
- Enterprise support and SLAs
Practical Reality
Most home enthusiasts and even small businesses will find the RTX 3090 setup more than adequate. The 20K cost difference is better spent on electricity bills (even with higher consumption) and other infrastructure improvements.