📍 Part of the Local LLMs in 2026 guide
Llama 3.1 70B needs 40 GB VRAM at Q4. The RTX 3090 24GB has 24 GB.
● Llama 3.1 70B (Meta) is a 70B parameter model used for Near-GPT-4 quality for complex tasks. Frontier-class open model, best for demanding use cases.
VRAM Requirements
| Quantization | VRAM Needed | RTX 3090 24GB |
|---|---|---|
| Q4_K_M (recommended) | 40 GB | ❌ |
| Q8_0 (high quality) | 72 GB | ❌ |
Why It Won’t Fit
Llama 3.1 70B needs 40 GB VRAM at Q4 quantization, but the RTX 3090 24GB only has 24 GB. You’re 16 GB short.
Options: You can run it with CPU offloading (expect ~8 tok/s — very slow), or upgrade to a GPU with 40+ GB VRAM.
About the RTX 3090 24GB
Pros: 24GB sweet spot for 27-34B models, great used value
Cons: Power hungry (350W), loud, large card
Price: ~$800 used — Check current price on Amazon →
Try It Yourself
🎯 LLM Hardware Checker
Select your exact GPU + RAM and see ALL models you can run.
💾 VRAM Calculator
Pick any model, see exact VRAM at Q4/Q5/Q8/FP16 with context scaling.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.