Run 70B Models Locally
Plan hardware and quantization for large local models
- 70B-local success is driven by VRAM headroom and quantization planning
- 24GB-class GPUs are the practical single-card baseline
- Multi-GPU adds capacity but also operational complexity
- Benchmark with realistic prompts and context lengths
- Use a clear quality-vs-latency go/no-go decision before upgrading
Hard Requirements
Running 70B models is primarily a memory planning problem before it is a compute problem.
VRAM and Quantization
Plan around aggressive quantization and leave memory headroom for context growth and runtime overhead.
System Balance
Use sufficient system RAM and fast NVMe storage to reduce model load bottlenecks and avoid unstable swaps.
Single-GPU Viability
A single 24GB-class GPU is the minimum practical path for many local 70B inference setups.
Consumer Path
RTX 4090 remains a common single-card baseline. RTX 5090-class options can improve headroom and throughput.
RTX 4090
RTX 5090
Multi-GPU Options
Multi-GPU can unlock higher capacity but increases operational complexity.
When to Scale Out
Scale to multi-GPU when single-card memory ceilings block your target context and quality profile.
Complexity Cost
Expect additional setup work for runtime compatibility, sharding behavior, and observability.
Throughput Expectations
70B user experience depends on sustained tokens/sec under realistic prompt lengths, not short synthetic tests.
Benchmark Correctly
Measure with long-context prompts and repeated runs to capture thermal and memory behavior over time.
Go/No-Go Decision Framework
Use objective criteria before committing budget to 70B-local infrastructure.
Framework
Proceed only if 70B quality gains are material for your use case and the resulting latency remains acceptable.
Frequently Asked Questions
Related Guides & Resources
Ready to Get Started?
Check our step-by-step setup guides and GPU recommendations.