Llama 3.1 70B (lorablated) by mlabonne is a large language model designed for advanced text generation tasks. With 70 billion parameters, this model excels in generating coherent, contextually rich text across a wide range of topics, making it suitable for applications such as content creation, chatbots, and natural language understanding. The lorablated version specifically aims to enhance the model’s performance through low-rank adaptation, which can improve its efficiency and effectiveness in fine-tuning scenarios without significantly increasing the computational load.
Compared to other models in its size class, Llama 3.1 70B holds its own, offering competitive performance and efficiency. It punches above its weight in terms of contextual understanding and generative capabilities, thanks to its extensive parameter count and the optimization techniques applied. However, the model’s massive size means it requires substantial hardware resources, with VRAM requirements ranging from 40.1 to 140.5 GB. This makes it more accessible to users with high-end GPUs or multi-GPU setups. For those who have the necessary hardware, this model is an excellent choice for demanding text generation tasks where high accuracy and context retention are crucial.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| BF16 | 16 | 140 GB | 140.5 GB | 141 GB | 100% |
| Q4_K_M | 4.5 | 39.6 GB | 40.1 GB | 40.6 GB | 85% |
Context window & KV cache
Adds 2.50 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 70B (lorablated)
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
bartowski/Llama-3.1-70B-Instruct-lorablated-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 70B (lorablated) 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 70B (lorablated)for many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
42.7 GB
40.1 GB weights + 2.1 GB KV
Aggregate tok/s
1
across 1 user
Per-user tok/s
1
70 B dense
⚠ Will spill 18.7 GB of weights to system RAM (~5× slower per offloaded layer). Use llama.cpp’s --cpu-offload-gb or vLLM’s --swap-space.
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 70B (lorablated)?
Llama 3.1 70B (lorablated) requires 40.1 GB VRAM minimum with BF16 quantization. For full precision you need 140.5 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 70B (lorablated)?
To run Llama 3.1 70B (lorablated), you need a GPU with at least 40.1 GB of VRAM, but up to 140.5 GB depending on the quantization level. NVIDIA A100 or V100 GPUs are recommended.
Is Llama 3.1 70B (lorablated) good for coding?
Llama 3.1 70B (lorablated) is highly effective for coding tasks due to its large context length and advanced language understanding, making it suitable for code generation and debugging.
Llama 3.1 70B (lorablated) vs Llama 3.1 8B?
Llama 3.1 70B (lorablated) offers significantly better performance and more detailed responses compared to Llama 3.1 8B, but requires much more VRAM and computational resources.
Can I run Llama 3.1 70B (lorablated) on a Mac?
Running Llama 3.1 70B (lorablated) on a Mac is possible with an M1/M2 chip or an external GPU, but it may require additional setup and may not be as efficient as on a dedicated GPU system.
How much VRAM does Llama 3.1 70B (lorablated) need?
Llama 3.1 70B (lorablated) requires between 40.1 GB and 140.5 GB of VRAM, depending on the quantization level used.
Is Llama 3.1 70B (lorablated) censored?
Llama 3.1 70B (lorablated) has had refusal-removal applied, which means it is less likely to refuse to generate content, but it still adheres to ethical guidelines and content policies.
Is Llama 3.1 70B (lorablated) commercial-use allowed?
Yes, Llama 3.1 70B (lorablated) is licensed under the llama3.1 license, which allows commercial use, provided you comply with the terms of the license.
Llama 3.1 70B (lorablated) context length?
Llama 3.1 70B (lorablated) has a context length of 131,072 tokens, allowing it to process very long sequences of text.
Does Llama 3.1 70B (lorablated) support function calling?
Llama 3.1 70B (lorablated) supports function calling, enabling it to interact with external systems and APIs, enhancing its capabilities in various applications.
Llama 3.1 70B (lorablated) quantization options?
Llama 3.1 70B (lorablated) supports multiple quantization levels, including 4-bit, 8-bit, and 16-bit, which reduce VRAM usage and improve inference speed while maintaining performance.
Can Llama 3.1 70B (lorablated) run on CPU?
While Llama 3.1 70B (lorablated) can technically run on a CPU, it is extremely resource-intensive and not practical for real-time inference. Using a GPU is strongly recommended.
Llama 3.1 70B (lorablated) fine-tuning?
Llama 3.1 70B (lorablated) can be fine-tuned using techniques like LoRA, which allow for efficient and targeted adjustments to the model without retraining the entire model.
Llama 3.1 70B (lorablated) system requirements?
To run Llama 3.1 70B (lorablated), you need a powerful GPU with 40.1 GB to 140.5 GB of VRAM, at least 256 GB of RAM, and a fast SSD for storage. A multi-core CPU is also beneficial.
Llama 3.1 70B (lorablated) performance benchmark?
Llama 3.1 70B (lorablated) can process around 50-100 tokens per second on a high-end GPU like the NVIDIA A100, depending on the quantization level and batch size.
Llama 3.1 70B (lorablated) for RAG?
Llama 3.1 70B (lorablated) is well-suited for Retrieval-Augmented Generation (RAG) tasks due to its large context length and ability to integrate external information seamlessly.
Llama 3.1 70B (lorablated) for agents?
Llama 3.1 70B (lorablated) can be used to create sophisticated conversational agents and chatbots, thanks to its advanced natural language processing capabilities and large context window.
Llama 3.1 70B (lorablated) for coding vs general?
Llama 3.1 70B (lorablated) performs exceptionally well in both coding and general tasks, but it excels in coding due to its specialized training and large context length.
Llama 3.1 70B (lorablated) vs ChatGPT?
Llama 3.1 70B (lorablated) offers a larger context length and more detailed responses compared to ChatGPT, but it requires more computational resources and is more complex to set up.
Llama 3.1 70B (lorablated) download size?
The download size for Llama 3.1 70B (lorablated) varies based on quantization, but it typically ranges from 35 GB to 100 GB, depending on the quantization level.
Best quant for Llama 3.1 70B (lorablated)?
The best quantization for Llama 3.1 70B (lorablated) depends on your specific needs. 8-bit quantization offers a good balance between performance and VRAM efficiency, while 4-bit is more memory-efficient but slightly less accurate.