📍 Part of the Local LLMs in 2026 guide
Llama 3.1 8B needs 5 GB VRAM at Q4. The RTX 3090 24GB has 24 GB.
● Llama 3.1 8B (Meta) is a 8B parameter model used for General chat, coding, instruction following. Strong all-rounder, comparable to GPT-3.5.
VRAM Requirements
| Quantization | VRAM Needed | RTX 3090 24GB |
|---|---|---|
| Q4_K_M (recommended) | 5 GB | ✅ |
| Q8_0 (high quality) | 8.5 GB | ✅ |
Expected Performance
Running Llama 3.1 8B at Q4 on the RTX 3090 24GB, expect approximately ~60 tokens/sec with Ollama or llama.cpp. That’s fast enough for interactive chat — you’ll see responses streaming in real-time.
Headroom: With 5 GB used out of 24 GB, you have 19 GB free for KV cache (context window). At 4K context, this is plenty. At 32K+ context you may need to reduce batch size.
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.