ai-gpu
Llama 3 8B VRAM Requirements
How much GPU VRAM do you need to run Meta Llama 3 8B? At FP16 it needs 16 GB. At INT4/GGUF Q4 it fits in just 4–5 GB — runnable on consumer GPUs.
Llama 3 8B: The Best Local LLM for Most Users
Llama 3 8B is the most accessible capable open-weight LLM available. It runs on consumer hardware, requires minimal setup with Ollama, and delivers quality close to GPT-3.5 on most tasks.
VRAM by Quantization
| Quantization | VRAM needed | Minimum GPU |
|---|---|---|
| FP16 | ~16 GB | RTX 4080 16GB, RTX 3090 |
| INT8 | ~8 GB | RTX 3070 8GB, RTX 4060 |
| INT4 / GGUF Q4 | ~4.5 GB | GTX 1070, RTX 3060 12GB |
| GGUF Q8_0 | ~8 GB | RTX 3070 8GB |
Quickest Setup: Ollama
# Install Ollama, then:
ollama run llama3
# Uses GGUF Q4 by default — ~4.5 GB VRAMLlama 3 8B vs 70B: When Does 8B Fall Short?
- ›Complex multi-step reasoning: 70B is noticeably better
- ›Code generation (>100 line functions): 70B handles context better
- ›Instruction following on ambiguous prompts: 70B more reliable
- ›Simple chat, Q&A, summarization, RAG: 8B is sufficient
Frequently Asked Questions
Can I run Llama 3 8B on a consumer GPU?
Yes. At GGUF Q4_K_M, Llama 3 8B requires only ~4.5 GB VRAM. It runs on a GTX 1070 (8GB), RTX 3060 12GB, RTX 4060, or any GPU with 6+ GB. Via Ollama: `ollama run llama3`.
What's the difference between Llama 3 8B and Llama 3 70B quality?
Llama 3 8B is excellent for most conversational tasks, coding assistance, and summarization. The 70B model noticeably outperforms on complex reasoning, multi-step math, and nuanced instruction following. For most local use cases, 8B INT4 is the right choice.
How much RAM does Llama 3 8B use on CPU?
llama.cpp can run Llama 3 8B in GGUF Q4 format using system RAM instead of VRAM. You need ~5 GB RAM for the model plus overhead. Performance is significantly slower than GPU — expect 3–15 tokens/sec on a modern CPU vs 50–100+ tokens/sec on an RTX 4090.
What context length can Llama 3 8B handle?
Llama 3 8B supports up to 8K context by default. With rope scaling tricks in llama.cpp, it can be extended to 32K or more, but quality degrades. At 8K context with batch size 1, expect ~1 GB KV cache overhead on top of the model weights.