Llama 3.1 8B Instruct by mlabonne is an 8 billion parameter language model designed for instruction-following tasks. This model excels in generating coherent and contextually relevant text, making it particularly useful for applications such as content creation, chatbots, and summarization. With a context length of 131,072 tokens, it can handle long-form inputs and outputs, which is beneficial for tasks requiring deep context understanding. The model is available in various quantizations, including BF16, Q4_K_M, and Q8_0, which allows for efficient deployment on a range of hardware setups with VRAM requirements ranging from 5.1 to 16.5 GB.
In its size class, Llama 3.1 8B Instruct holds its own, offering a balance between performance and resource efficiency. While it may not outperform the largest models in terms of raw capabilities, it provides a compelling alternative for those who need a powerful yet manageable model. Its efficiency makes it a practical choice for users with mid-range GPUs, ensuring that it can be deployed without requiring top-of-the-line hardware. This model is ideal for developers, researchers, and businesses looking to integrate advanced text generation capabilities into their projects without the overhead of more resource-intensive models.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| BF16 | 16 | 16 GB | 16.5 GB | 17 GB | 100% |
| Q4_K_M | 4.5 | 4.583 GB | 5.08 GB | 5.58 GB | 85% |
| Q8_0 | 8 | 7.954 GB | 8.45 GB | 8.95 GB | 98% |
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 (abliterated)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Llama 3.1 8B Instruct (abliterated) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Llama 3.1 8B Instruct (abliterated)for 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 (abliterated)?
Llama 3.1 8B Instruct (abliterated) requires 5.08 GB VRAM minimum with BF16 quantization. For full precision you need 16.5 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 (abliterated)?
To run Llama 3.1 8B Instruct (abliterated), you need a GPU with at least 5.1 GB of VRAM for the lowest quantization level, up to 16.5 GB for the highest. NVIDIA RTX 3060 or better is recommended.
Is Llama 3.1 8B Instruct (abliterated) good for coding?
Llama 3.1 8B Instruct (abliterated) is suitable for coding tasks, offering strong performance in generating code snippets and providing programming assistance.
Llama 3.1 8B Instruct (abliterated) vs Llama 3.1 8B?
Llama 3.1 8B Instruct (abliterated) is a modified version of Llama 3.1 8B that removes the 'I can't help with that' responses while retaining the instruct behavior. It is designed to be more helpful and less restrictive.
Can I run Llama 3.1 8B Instruct (abliterated) on a Mac?
Yes, you can run Llama 3.1 8B Instruct (abliterated) on a Mac with an M1 or M2 chip, provided you have the necessary software environment and sufficient VRAM.
How much VRAM does Llama 3.1 8B Instruct (abliterated) need?
The VRAM requirement for Llama 3.1 8B Instruct (abliterated) ranges from 5.1 GB to 16.5 GB, depending on the quantization level used.
Is Llama 3.1 8B Instruct (abliterated) censored?
Llama 3.1 8B Instruct (abliterated) has been modified to remove the 'I can't help with that' responses, making it less likely to refuse requests, but it still adheres to ethical guidelines.
Is Llama 3.1 8B Instruct (abliterated) commercial-use allowed?
Llama 3.1 8B Instruct (abliterated) is licensed under the llama3.1 license, which allows commercial use as long as you comply with the terms of the license.
Llama 3.1 8B Instruct (abliterated) context length?
Llama 3.1 8B Instruct (abliterated) has a context length of 131,072 tokens, allowing it to handle very long inputs and maintain context over extended conversations.
Does Llama 3.1 8B Instruct (abliterated) support function calling?
Llama 3.1 8B Instruct (abliterated) supports function calling, enabling it to interact with external systems and perform actions based on user input.
Llama 3.1 8B Instruct (abliterated) quantization options?
Llama 3.1 8B Instruct (abliterated) offers multiple quantization options, including 4-bit, 8-bit, and 16-bit, to balance between performance and memory usage.
Can Llama 3.1 8B Instruct (abliterated) run on CPU?
Yes, Llama 3.1 8B Instruct (abliterated) can run on CPU, but it will be significantly slower compared to running on a GPU.
Llama 3.1 8B Instruct (abliterated) fine-tuning?
Llama 3.1 8B Instruct (abliterated) can be fine-tuned using frameworks like Hugging Face Transformers, but it requires a powerful GPU and significant computational resources.
Llama 3.1 8B Instruct (abliterated) system requirements?
To run Llama 3.1 8B Instruct (abliterated), you need at least 16 GB of RAM, a multi-core CPU, and a GPU with 5.1 GB to 16.5 GB of VRAM, depending on the quantization level.
Llama 3.1 8B Instruct (abliterated) performance benchmark?
Performance benchmarks show that Llama 3.1 8B Instruct (abliterated) can process around 100-200 tokens per second on a high-end GPU, with lower performance on CPUs and less powerful GPUs.
Llama 3.1 8B Instruct (abliterated) for RAG?
Llama 3.1 8B Instruct (abliterated) can be used for Retrieval-Augmented Generation (RAG) tasks, enhancing its ability to generate accurate and contextually relevant responses by integrating external data sources.
Llama 3.1 8B Instruct (abliterated) for agents?
Llama 3.1 8B Instruct (abliterated) is well-suited for creating conversational agents and chatbots, thanks to its improved instruct behavior and reduced refusal rate.
Llama 3.1 8B Instruct (abliterated) for coding vs general?
Llama 3.1 8B Instruct (abliterated) performs well in both coding and general tasks, but it may excel more in coding due to its strong language generation capabilities and ability to produce code snippets.
Llama 3.1 8B Instruct (abliterated) vs ChatGPT?
Compared to ChatGPT, Llama 3.1 8B Instruct (abliterated) offers more flexibility in terms of quantization and fine-tuning, and it is less likely to refuse requests, making it a better choice for certain use cases.
Llama 3.1 8B Instruct (abliterated) download size?
The download size of Llama 3.1 8B Instruct (abliterated) varies depending on the quantization level, ranging from approximately 4 GB (4-bit) to 16 GB (16-bit).
Best quant for Llama 3.1 8B Instruct (abliterated)?
The best quantization level for Llama 3.1 8B Instruct (abliterated) depends on your hardware. For most users, 8-bit quantization provides a good balance between performance and memory usage, requiring about 8 GB of VRAM.