Best GPU for 70B Parameter Models (2026)
Run 70B LLMs like Llama 3.3 70B and DeepSeek R1 70B locally. Compare dual RTX 4090, RTX A6000, Apple M4 Max, and CPU-only options with real performance data.
Last updated: February 7, 2026
🎯 Why This Matters
70B parameter models like Llama 3.3 70B and DeepSeek R1 70B represent the sweet spot of open-source AI — they rival GPT-4 level performance on many tasks. But they need 40-48GB of memory at Q4 quantization, which no single consumer GPU provides. You'll need creative solutions: dual GPUs, workstation cards, Apple Silicon, or CPU offloading.
🏆 Our Recommendations
Tested and ranked by real-world AI performance
Apple M4 Max (128GB Unified Memory)
✅ Pros
- 128GB unified memory fits 70B easily
- Silent operation
- No multi-GPU headaches
- Energy efficient (~100W total system)
- Also a great workstation
❌ Cons
- $3,499 for Mac Studio config
- Slower than dual NVIDIA GPUs
- Can't upgrade memory later
- Limited to MLX/llama.cpp frameworks
2x NVIDIA RTX 4090 48GB Total
✅ Pros
- Fastest consumer option for 70B
- 48GB total VRAM
- Can also run smaller models on single GPU
- Strong community support
❌ Cons
- $3,200 for both cards
- 900W total GPU power draw
- Needs large case and 1600W+ PSU
- Model splitting adds complexity
- Some frameworks don't support multi-GPU well
NVIDIA RTX A6000 48GB
✅ Pros
- 48GB on a single card — simplest GPU solution
- 300W TDP is manageable
- ECC memory for reliability
- Professional-grade reliability
❌ Cons
- $4,200 is expensive
- Slower than dual 4090 setup
- No display outputs on some models
- Hard to find at retail
CPU-Only: 128GB DDR5 RAM System
✅ Pros
- Cheapest way to run 70B models
- 128GB RAM fits any quantization
- No GPU needed
- Can upgrade to GPU later
❌ Cons
- Very slow: 3-5 tok/s
- Not suitable for interactive chat
- High RAM cost
- CPU-bound generation
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🤖 Compatible Models
Models you can run with this hardware
❓ Frequently Asked Questions
Can I run 70B models on a single GPU?
Not on consumer GPUs. The RTX 4090 has 24GB VRAM, but 70B Q4 needs ~40GB. The only single consumer-ish card that works is the RTX A6000 (48GB) at ~$4,200. For cheaper options, use dual GPUs or Apple Silicon with high unified memory.
Is CPU inference viable for 70B models?
It works but is slow — expect 3-5 tokens/sec with 128GB DDR5 RAM. That's usable for batch processing or code generation where you can wait, but not great for interactive chat. Many people use CPU inference overnight for longer tasks.
How does Apple M4 Max compare to NVIDIA for 70B?
Apple M4 Max with 128GB unified memory gives ~12-15 tok/s on 70B Q4 — slower than dual RTX 4090 (~20-25 tok/s) but much simpler to set up. The Mac is silent, energy efficient, and just works. If you value simplicity over raw speed, Apple Silicon is excellent.
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