How Much VRAM Does Llama 3.1 8B Need to Run Locally?
See devices that run itLlama 3.1 8B is a dense model with 8B parameters. At Q4 it needs at least 7GB of VRAM, and 8GB is the comfortable amount for an 8K context. The cheapest device that holds it is the GeForce RTX 4080, with no used-price data yet, at roughly 91.6 tokens/s.
Llama 3.1 8B details
VRAM needed for Llama 3.1 8B by quantization and context
Total at 8K| Quantization | WeightsGB | Total at 8KGB | Total at 32KGB | Total at 128KGB |
|---|---|---|---|---|
| Q4_K_M | 4.9GGUF | 6.5 | 9.5 | 21.5 |
| Q5_K_M | 5.7GGUF | 7.4 | 10.4 | 22.4 |
| Q8_0 | 8.5GGUF | 10.3 | 13.3 | 25.3 |
| FP16 | 15.7est. | 17.7 | 20.7 | 32.7 |
Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 128K limit show a dash.
Which GPUs can run Llama 3.1 8B?
Q4_K_M · 8K context · sorted by eBay used price| Device | VRAMGB | Used price | Est. t/s | Suggested context |
|---|---|---|---|---|
 GeForce RTX 4080 | 16 | —No listings | 91.6 | 32K |
| GeForce RTX 5070 Ti | 16 | —No listings | 112.4 | 32K |
| GeForce RTX 5080 | 16 | —No listings | 119.7 | 32K |
 Radeon RX 9070 XT | 16 | —No listings | 60 | 32K |
| Radeon RX 7900 XT | 20 | —No listings | 74.1 | 32K |
 Arc Pro B60 | 24 | —No listings | 39.2 | 128K |
| GeForce RTX 3090 | 24 | —No listings | 117 | 128K |
| GeForce RTX 4090 | 24 | —No listings | 125.1 | 128K |
| GeForce RTX 5090 D V2 | 24 | —No listings | 161.4 | 128K |
| Radeon RX 7900 XTX | 24 | —No listings | 87.9 | 128K |
| RTX PRO 4000 Blackwell | 24 | —No listings | 86.3 | 128K |
| Arc Pro B65 | 32 | —No listings | 51.7 | 128K |
| Arc Pro B70 | 32 | —No listings | 51.7 | 128K |
| GeForce RTX 4080 SUPER 32GB (modded) | 32 | —No listings | 93.9 | 128K |
| GeForce RTX 5090 | 32 | —No listings | 206.3 | 128K |
| GeForce RTX 5090 D | 32 | —No listings | 206.3 | 128K |
| Instinct MI100 | 32 | —No listings | 110.3 | 128K |
| Radeon AI PRO R9700 | 32 | —No listings | 60 | 128K |
| Radeon PRO W7800 | 32 | —No listings | 54.3 | 128K |
| RTX PRO 4500 Blackwell | 32 | —No listings | 112.4 | 128K |
| A100 40GB PCIe | 40 | —No listings | 183 | 128K |
| GeForce RTX 4090 48GB (modded) | 48 | —No listings | 125.1 | 128K |
| Radeon PRO W7900 | 48 | —No listings | 79.7 | 128K |
| RTX PRO 5000 Blackwell 48GB | 48 | —No listings | 161.4 | 128K |
| Instinct MI210 | 64 | —No listings | 142.9 | 128K |
| RTX PRO 5000 Blackwell 72GB | 72 | —No listings | 161.4 | 128K |
| Mac Studio M4 Max 128GB | 128 | —No listings | 56.5 | 128K |
| Mac Studio M3 Ultra 256GB | 256 | —No listings | 82.8 | 128K |
| Mac Studio M3 Ultra 512GB | 512 | —No listings | 82.8 | 128K |
Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.
Run Llama 3.1 8B with Ollama, llama.cpp or vLLM
Ollama pulls and runs it in one command; the quantization comes from the official tag.
ollama run llama3.1:8b
llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.
llama-server -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M -c 8192
vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.
vllm serve meta-llama/Llama-3.1-8B-Instruct
File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology
FAQ
How much VRAM does Llama 3.1 8B need?
At Q4_K_M with an 8K context Llama 3.1 8B needs about 7GB of VRAM; 8GB leaves comfortable headroom.
What is the cheapest GPU that runs Llama 3.1 8B?
The GeForce RTX 4080: 16GB of VRAM at roughly 91.6 tokens/s. We have no used-price data for it yet.
Can you run Llama 3.1 8B with Ollama?
Yes: ollama run llama3.1:8b.