# Llama 3.1 8B Instruct: hardware requirements
> Llama 3.1 8B Instruct (Meta) is a 8B dense model with a 128K-token context window, released under the Llama 3.1 Community License. It needs about 6.7GB of memory at Q4_K_M with 8K context.
Llama 3.1 8B was the default small local model through 2024 and 2025, and it remains a common baseline for fine-tunes and benchmarks. Newer models of the same size, such as Qwen3.5 9B and Gemma 4 E4B, are clearly better at reasoning, coding, and vision, so it is now superseded for new setups. At Q4_K_M it needs about 7GB with 8K context, which fits any 8GB GPU or 16GB Mac, and a 12GB card can run the near-lossless Q8_0. Its 128K context is real, but the KV cache grows to about 17GB at full length.

## Recommended hardware
- **Minimum GPU:** RTX 5060 (8GB), Q4_K_M, ~68 tokens/sec
- **Best value:** RTX 5060 (8GB), Q4_K_M, ~68 tokens/sec
- **Fastest single GPU:** RTX 5090 (32GB), Q8_0, ~145 tokens/sec
- **Mac / unified memory:** Ryzen AI Max+ 395 (128GB), Q8_0, ~22 tokens/sec

## Memory by quantization
| Quant | Weights | Total @ 8K ctx | Total @ 32K ctx |
| --- | --- | --- | --- |
| Q2_K | 3.0GB | 4.8GB | 8.0GB |
| Q3_K_M | 4.0GB | 5.7GB | 9.0GB |
| Q4_K_M | 4.9GB | 6.7GB | 10.0GB |
| Q5_K_M | 5.7GB | 7.6GB | 10.8GB |
| Q6_K | 6.6GB | 8.5GB | 11.7GB |
| Q8_0 | 8.5GB | 10.5GB | 13.7GB |
| BF16 | 16.1GB | 18.2GB | 21.5GB |

## GPUs that run it fully in memory (fastest first)
| GPU | Memory | Quant | Speed | Price |
| --- | --- | --- | --- | --- |
| RTX 5090 | 32GB | Q8_0 | ~145 tok/s | ~$4,300 |
| RTX PRO 6000 | 96GB | Q8_0 | ~145 tok/s | ~$16,000 |
| RTX PRO 5000 72GB | 72GB | Q8_0 | ~110 tok/s | ~$9,100 |
| RTX 4090 | 24GB | Q8_0 | ~86 tok/s | ~$2,500 used |
| RTX 5080 | 16GB | Q8_0 | ~83 tok/s | ~$1,600 |
| RTX 6000 Ada | 48GB | Q8_0 | ~83 tok/s | ~$4,750 used |
| RTX 5070 Ti | 16GB | Q8_0 | ~77 tok/s | ~$1,200 |
| RTX 3090 | 24GB | Q8_0 | ~73 tok/s | ~$1,050 used |
| M5 Ultra 512GB | 512GB | Q8_0 | ~71 tok/s | n/a |
| M5 Ultra 256GB | 256GB | Q8_0 | ~71 tok/s | ~$9,499 |
| RTX 5060 | 8GB | Q4_K_M | ~68 tok/s | ~$450 |
| RX 7900 XTX | 24GB | Q8_0 | ~66 tok/s | ~$1,050 used |
| RTX 4080 Super | 16GB | Q8_0 | ~64 tok/s | ~$1,150 used |
| V100 32GB | 32GB | Q8_0 | ~63 tok/s | ~$700 used |
| RTX A6000 | 48GB | Q8_0 | ~61 tok/s | ~$4,400 used |
| RTX 4070 Ti Super | 16GB | Q8_0 | ~59 tok/s | ~$900 used |
| RTX 5070 | 12GB | Q8_0 | ~59 tok/s | ~$800 |
| M3 Ultra 512GB | 512GB | Q8_0 | ~49 tok/s | $9,499 MSRP |
| MI50 32GB | 32GB | Q8_0 | ~49 tok/s | ~$600 used |
| R9700 | 32GB | Q8_0 | ~45 tok/s | ~$1,800 |
| RX 9070 XT | 16GB | Q8_0 | ~45 tok/s | ~$800 |
| RTX 4070 | 12GB | Q8_0 | ~45 tok/s | ~$560 used |
| M5 Max 128GB | 128GB | Q8_0 | ~44 tok/s | ~$5,100 |
| RTX 5060 Ti 16GB | 16GB | Q8_0 | ~40 tok/s | ~$620 |
| M4 Max 128GB | 128GB | Q8_0 | ~39 tok/s | $3,499 MSRP |

## How to run it
- Ollama: `ollama run llama3.1:8b`
- llama.cpp: `llama-server -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M -c 8192 -ngl 99`
- MLX (Mac): `mlx_lm.chat --model mlx-community/Meta-Llama-3.1-8B-Instruct-4bit`
- Weights: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

## Strengths
- Runs on any 8GB GPU at Q4_K_M with room for 8K context
- The most fine-tuned base model around, with thousands of community variants
- Mature support in every runtime, from llama.cpp and Ollama to vLLM and MLX
- Predictable, well-understood behavior for chat and simple writing tasks

## Weaknesses
- Clearly behind 2026 models of the same size on reasoning, math, and coding
- Text only, with no vision input and no reasoning mode
- Large KV cache for long contexts (about 1.1GB per 8K tokens)
- Llama license imposes use restrictions and attribution requirements
Full page: https://llamabuilds.ai/models/llama-3-1-8b · Hardware guide: https://llamabuilds.ai/models/llama-3-1-8b/requirements

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Source: https://llamabuilds.ai/models/llama-3-1-8b · Updated October 14, 2026 · Llama Builds. Speeds marked ~ are estimates from our bandwidth model (https://llamabuilds.ai/methodology); measured results are labeled as such.