Llama 3.1 8B Instruct is a robust language model developed by Meta, designed to excel in a variety of text generation tasks. With 8 billion parameters, this model offers a balance between performance and resource requirements, making it suitable for generating coherent and contextually relevant text across a wide range of applications, from chatbots and content creation to summarization and translation. The model's context length of 131,072 tokens allows it to handle long-form text, which is particularly useful for tasks requiring deep contextual understanding.
In its size class, Llama 3.1 8B Instruct holds its own, often outperforming models with similar parameter counts in terms of both quality and efficiency. It punches above its weight in generating nuanced and detailed responses, while maintaining a relatively low memory footprint compared to larger models. This makes it an attractive choice for users who need high-quality text generation without the need for extensive computational resources. The available quantizations, including Q4_K_M, Q5_K_M, Q8_0, and FP16, further enhance its efficiency, allowing it to run smoothly on a variety of hardware setups, from mid-range GPUs with 5.1 GB VRAM to more powerful systems with up to 17.0 GB VRAM. Ideal users include developers, researchers, and businesses looking for a versatile and efficient text generation solution that can be deployed on a range of hardware, from personal computers to cloud servers.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.583 GB | 5.08 GB | 5.58 GB | 85% |
| Q5_K_M | 5.5 | 5.339 GB | 5.84 GB | 6.34 GB | 90% |
| Q8_0 | 8 | 7.954 GB | 8.45 GB | 8.95 GB | 98% |
| FP16 | 16 | 16 GB | 17 GB | 20 GB | 100% |
Context window & KV cache
Adds 1.00 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 128K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Llama 3.1 8B Instruct
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull llama3.1:8b - 2
Chat
ollama run llama3.1:8b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"llama3.1:8b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Llama 3.1 8B Instruct on actual hardware.
| GPU | Median tok/s | Reports | Typical setup |
|---|---|---|---|
| H100 SXM | 245.0 | 1 | Q4_K_M · vLLM · Linux · 8K ctx |
| A100 80GB | 165.0 | 1 | Q4_K_M · vLLM · Linux · 8K ctx |
| RTX 4090 | 95.5 | 2 | Q4_K_M · llama.cpp · Linux · 4K ctx |
| RTX 3090 | 71.8 | 1 | Q4_K_M · Ollama · Linux · 4K ctx |
| RTX 4060 Ti | 51.4 | 1 | Q4_K_M · Ollama · Windows · 4K ctx |
| M3 Max | 47.5 | 1 | Q4_K_M · MLX · macOS · 4K ctx |
| M2 Pro | 27.1 | 1 | Q4_K_M · Ollama · macOS · 4K ctx |
Self-host serving plan
Want to host Llama 3.1 8B Instructfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
6.3 GB
5.1 GB weights + 0.7 GB KV
Aggregate tok/s
31
across 1 user
Per-user tok/s
31
8 B dense
✅ Fits in 24 GB VRAM with 17.7 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct requires 5.08 GB VRAM minimum with Q4_K_M quantization. For full precision you need 17 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Llama 3.1 8B Instruct?
To run Llama 3.1 8B Instruct, you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 17.0 GB for full precision.
Is Llama 3.1 8B Instruct good for coding?
Llama 3.1 8B Instruct is well-suited for coding tasks, offering a good balance of performance and efficiency for generating code and providing programming assistance.
Llama 3.1 8B Instruct vs Llama 3.1 8B?
Llama 3.1 8B Instruct is an instruction-tuned version of Llama 3.1 8B, making it better suited for following user instructions and generating more coherent and contextually relevant responses.
Can I run Llama 3.1 8B Instruct on a Mac?
Yes, you can run Llama 3.1 8B Instruct on a Mac with an M1 or M2 chip, provided you have the necessary VRAM and system resources.
How much VRAM does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct requires between 5.1 GB and 17.0 GB of VRAM, depending on the quantization level used.
Is Llama 3.1 8B Instruct censored?
Llama 3.1 8B Instruct is not inherently censored, but it may include content filters to prevent harmful or inappropriate outputs.
Is Llama 3.1 8B Instruct commercial-use allowed?
Llama 3.1 8B Instruct is licensed under the llama3.1 license, which allows for commercial use, but you should review the specific terms to ensure compliance.
Llama 3.1 8B Instruct context length?
Llama 3.1 8B Instruct has a context length of 131,072 tokens, allowing it to handle very long sequences of text.
Does Llama 3.1 8B Instruct support function calling?
Yes, Llama 3.1 8B Instruct supports function calling, enabling it to interact with external systems and APIs.
Llama 3.1 8B Instruct quantization options?
Llama 3.1 8B Instruct supports multiple quantization levels, including INT8, INT4, and FP16, to optimize performance and VRAM usage.
Can Llama 3.1 8B Instruct run on CPU?
Yes, Llama 3.1 8B Instruct can run on a CPU, but it will be significantly slower compared to running on a GPU.
Llama 3.1 8B Instruct fine-tuning?
Llama 3.1 8B Instruct can be fine-tuned on your own data to improve its performance on specific tasks or domains.
Llama 3.1 8B Instruct system requirements?
Llama 3.1 8B Instruct requires a minimum of 5.1 GB of VRAM, 16 GB of RAM, and a multi-core CPU. For optimal performance, a high-end GPU with at least 16 GB of VRAM is recommended.
Llama 3.1 8B Instruct performance benchmark?
Llama 3.1 8B Instruct can process around 100-200 tokens per second on a high-end GPU, with performance varying based on the quantization level and hardware configuration.
Llama 3.1 8B Instruct for RAG?
Llama 3.1 8B Instruct can be used for Retrieval-Augmented Generation (RAG) to enhance its context and generate more accurate and relevant responses.
Llama 3.1 8B Instruct for agents?
Llama 3.1 8B Instruct is suitable for creating conversational agents, as it can generate natural and contextually appropriate responses in dialogue settings.
Llama 3.1 8B Instruct for coding vs general?
Llama 3.1 8B Instruct performs well in both coding and general tasks, but it may excel more in coding due to its instruction-tuned nature and ability to follow complex instructions.
Llama 3.1 8B Instruct vs ChatGPT?
Llama 3.1 8B Instruct offers a good balance of performance and efficiency, while ChatGPT may have a larger model size and potentially better performance in certain areas, but with higher resource requirements.
Llama 3.1 8B Instruct download size?
The download size of Llama 3.1 8B Instruct varies based on the quantization level, ranging from approximately 10 GB for the highest compression to 32 GB for the full precision model.
Best quant for Llama 3.1 8B Instruct?
The best quantization level for Llama 3.1 8B Instruct depends on your hardware and performance needs. INT8 is a good balance, offering significant VRAM savings with minimal impact on performance.