Meta · Released July 2024
Llama 3.1 8B Instruct
What it takes to run Llama 3.1 8B Instruct locally: memory by quantization, the GPUs that handle it, and how fast it runs.
Updated
- Parameters
- 8B
- Architecture
- Dense
- Context
- 128K tokens
- License
- Llama 3.1 Community License
- Memory at Q4_K_M
- ~6.7GB
What do you need to run Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct needs about 6.7GB of VRAM at Q4_K_M with 8K context (4.9GB for the weights plus KV cache and overhead). The smallest GPU that runs it comfortably is a 8GB card like the RTX 5060.
What to buy
GPUs for Llama 3.1 8B Instruct
Speeds are estimates at the quantization shown with 8K context. We may earn a commission when you buy through these links. Learn more.
Memory
Llama 3.1 8B Instruct VRAM requirements by quantization
Lower-bit quantizations shrink the model at some cost to quality. Q4_K_M is the usual sweet spot. Totals include the KV cache for the context length shown plus runtime overhead.
| Quantization | Quality | Weights | Total @ 8K ctx | Total @ 32K ctx |
|---|---|---|---|---|
| Q2_K | Rough | 3.0 GB | 4.8 GB | 8.0 GB |
| Q3_K_M | Usable | 4.0 GB | 5.7 GB | 9.0 GB |
| Q4_K_M | RecommendedDefault | 4.9 GB | 6.7 GB | 10.0 GB |
| Q5_K_M | Very good | 5.7 GB | 7.6 GB | 11 GB |
| Q6_K | Near-lossless | 6.6 GB | 8.5 GB | 12 GB |
| Q8_0 | Practically lossless | 8.5 GB | 10 GB | 14 GB |
| BF16 | Full precision | 16 GB | 18 GB | 21 GB |
KV cache note: Grouped-query attention with 8 KV heads: 2 x 32 layers x 8 x 128 dims x 2 bytes = 131,072 bytes (128 KiB) per token. That is about 1.1GB at 8K context and 17GB at the full 128K.
Performance
How fast Llama 3.1 8B Instruct runs
Single-user generation speed on popular GPUs that fit the model entirely in memory, at the best quantization each can hold.
RTX 5090
Q8_0 · 32GB
~145tok/s
RTX PRO 6000
Q8_0 · 96GB
~145tok/s
RTX PRO 5000 72GB
Q8_0 · 72GB
~110tok/s
RTX 4090
Q8_0 · 24GB
~86tok/s
RTX 5080
Q8_0 · 16GB
~83tok/s
RTX 6000 Ada
Q8_0 · 48GB
~83tok/s
RTX 5070 Ti
Q8_0 · 16GB
~77tok/s
RTX 3090
Q8_0 · 24GB
~73tok/s
M5 Ultra 512GB
Q8_0 · 512GB
~71tok/s
M5 Ultra 256GB
Q8_0 · 256GB
~71tok/s
Measured
Llama 3.1 8B Instruct benchmarks
- RTX 6000 AdaQ4_K_M · llama.cpp135 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 4090Q4_K_M · llama.cpp130 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 3090Q4_K_M · llama.cpp115 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX A6000Q4_K_M · llama.cpp105 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 4000 AdaQ4_K_M · llama.cpp59 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 4090F16 · llama.cpp55 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 6000 AdaF16 · llama.cpp52 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 3090F16 · llama.cpp47 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX A6000F16 · llama.cpp41 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
- RTX 4000 AdaF16 · llama.cpp21 tok/s XiongjieDai/GPU-Benchmarks-on-LLM-Inference (GitHub)
Overview
About Llama 3.1 8B Instruct
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.
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
Setup
How to run Llama 3.1 8B Instruct
$ ollama run llama3.1:8b$ llama-server -hf bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M -c 8192 -ngl 99$ mlx_lm.chat --model mlx-community/Meta-Llama-3.1-8B-Instruct-4bitWeights: meta-llama/Llama-3.1-8B-Instruct · GGUF: bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
Alternatives
Models like Llama 3.1 8B Instruct
Frequently asked questions
How much VRAM do you need to run Llama 3.1 8B Instruct?
About 6.7GB at Q4_K_M with 8K tokens of context: 4.9GB of weights, 1.1GB of KV cache, and 0.7GB of runtime overhead. Higher-precision quants and longer contexts need more; see the table above.
What is the cheapest GPU that can run Llama 3.1 8B Instruct?
The smallest card in our database that runs it comfortably is the RTX 5060 (8GB), at Q4_K_M and ~68 tokens/sec.
Is Llama 3.1 8B Instruct free for commercial use?
Llama 3.1 8B Instruct is released under the Llama 3.1 Community License. Commercial use is allowed with some conditions; read the license before shipping a product.
How long a context does Llama 3.1 8B Instruct support?
Up to 131,072 tokens (128K). Long contexts need a lot of extra memory for the KV cache, so most local setups run 8K to 32K.
Sources: Llama 3.1 8B Instruct model card (Hugging Face), modelfit.io GGUF file sizes for Llama 3.1 8B. Capability score basis: Llama Builds scale anchor (35 to 40 band), consistent with its Artificial Analysis Intelligence Index v3 standing (October 2025).