Best GPU for Llama 3 70B Under $500
Llama 3 70B is the model everyone wants to run locally. It's close to GPT-3.5 quality in many benchmarks and represents the sweet spot of open-source AI capability. But 70B parameters need a lot of VRAM. Let's look at what's realistically possible under $500.
How Much VRAM Does 70B Need?
- FP16 (full precision): 70B x 2 bytes = ~140GB - Not happening on consumer hardware
- Q8 (8-bit): ~70GB - Still needs multiple high-end GPUs
- Q4 (4-bit): ~38-42GB (with overhead) - This is the target
- Q3: ~30-35GB - Feasible with quality tradeoffs
- Q2: ~25-28GB - Fits in 24GB with aggressive settings, but noticeably degraded quality
The realistic minimum for running 70B with acceptable quality (Q4) is approximately 40GB of total VRAM. No single GPU under $500 offers this, so we need to get creative.
Option 1: Dual Tesla P40 (48GB Total)
2x Tesla P40 24GB
~$300 total ($150 each)
Estimated speed: 8-15 tok/s for 70B Q4
Two P40s give you 48GB of VRAM - enough for 70B at Q4 quantization with room for context. This is the cheapest way to run 70B locally with the full model in VRAM.
- Pros: Cheapest 48GB setup. Full model in VRAM. No CPU offloading needed.
- Cons: Slow generation (~10 tok/s) due to PCIe inter-GPU transfer. Need motherboard with 2 x16 PCIe slots (or x16 + x8). 500W combined TDP. Passive cooling for both cards.
- Software: llama.cpp supports multi-GPU out of the box with
--tensor-split
Requirements for Dual P40
- Motherboard: Needs 2 PCIe x16 slots with enough spacing. Server/workstation boards (like Supermicro X10/X11 series) work best.
- PSU: At least 850W recommended. Each P40 has an 8-pin EPS connector (not standard PCIe 8-pin on some variants - check your specific card).
- CPU: Any modern CPU with enough PCIe lanes (Xeon E5 v3/v4 is the budget pairing).
- Cooling: Both cards need aftermarket cooling. A well-ventilated case is essential.
- RAM: At least 32GB system RAM for llama.cpp overhead.
Option 2: RTX 3090 + Heavy Quantization
RTX 3090 24GB (Used)
~$650-750 (over budget, but worth mentioning)
Estimated speed: 3-5 tok/s for 70B Q2 with CPU offloading
A single RTX 3090 can technically run 70B, but only at extremely aggressive quantization (Q2) with most layers offloaded to CPU. Not recommended for interactive use.
- Reality check: Even with Q2 quantization (~25GB), you'll exceed 24GB with KV cache and need CPU offloading. Speed drops to 3-5 tok/s, which is painfully slow.
- Better use: An RTX 3090 excels at running 7B-30B models at high quality. It's a poor choice specifically for 70B.
Option 3: CPU Offloading
Single P40 + 64GB System RAM
~$200 (P40 $150 + extra RAM ~$50)
Estimated speed: 2-4 tok/s for 70B Q4
llama.cpp allows splitting model layers between GPU and CPU. Put what fits in the GPU (24GB worth of layers), offload the rest to system RAM.
- Pros: Cheapest possible way to "run" 70B. Only one GPU needed.
- Cons: Extremely slow. DDR4 bandwidth (~40 GB/s) is the bottleneck for offloaded layers. Speed is dominated by the slowest component.
- Verdict: Technically works, but at 2-4 tok/s it's not practical for conversational use. Fine for one-off batch tasks where you can wait.
Comparison Table
| Setup | Total VRAM | Cost | 70B Q4 Speed | Practical? |
|---|---|---|---|---|
| 2x Tesla P40 | 48GB | ~$300 | 8-15 tok/s | Usable |
| P40 + M40 | 48GB | ~$230 | 6-10 tok/s | Barely |
| RTX 3090 (Q2 + offload) | 24GB + RAM | ~$700 | 3-5 tok/s | Painful |
| P40 + CPU offload | 24GB + RAM | ~$200 | 2-4 tok/s | Not really |
| 2x RTX 3090 | 48GB | ~$1400 | 20-30 tok/s | Great (but over budget) |
The Honest Answer: Consider Smaller Models
Unpopular Opinion: 70B on a Budget Isn't Worth It
Here's the truth most guides won't tell you: running 70B under $500 gives you a poor experience. At 8-15 tok/s (best case with dual P40), it feels sluggish compared to ChatGPT or Claude. And you're dealing with dual-GPU complexity, cooling challenges, and high power consumption.
Smaller models have improved dramatically. Consider these alternatives:
| Model | VRAM Needed (Q4) | GPU | Est. Speed | Quality |
|---|---|---|---|---|
| Llama 3 8B | ~5GB | P40 ($150) | 45 tok/s | Good for simple tasks |
| Llama 3 14B | ~8GB | P40 ($150) | 30 tok/s | Solid all-around |
| Qwen 2.5 32B | ~18GB | P40 ($150) | 15 tok/s | Excellent - rivals 70B on many tasks |
| Deepseek V2.5 Lite 16B | ~9GB | P40 ($150) | 28 tok/s | Great for coding |
| Llama 3 70B Q4 | ~40GB | 2x P40 ($300) | 10 tok/s | Best open-source quality |
A single Tesla P40 running Qwen 2.5 32B at Q4 gives you near-70B quality at 15 tok/s for $150. That's a much better experience than dual P40s struggling with 70B at 10 tok/s for $300.
Our Recommendation
If you must run 70B: Dual Tesla P40s ($300) is the cheapest viable option. You'll get 8-15 tok/s, which is usable but not great. Make sure you have a motherboard with proper PCIe lane support and adequate cooling.
Better value play: Buy a single Tesla P40 for $150 and run the best 24B-32B model you can. You'll get faster speeds, simpler setup, less power draw, and quality that's surprisingly close to 70B for most practical tasks.
Check Current Prices
GPU prices change frequently on the used market. Check GPUDojo for current P40, M40, and other GPU listings with real-time pricing.