LLaMA 3 70B
LLaMA 3 70B is Meta’s state-of-the-art open-weight large language model with 70 billion parameters and an 8K token context window. It’s trained on ~15 trillion tokens for top-tier performance in reasoning, code generation, and language tasks. While it rivals GPT-4 in many benchmarks, it requires high-end hardware for inference—typically 2x 80GB GPUs or quantized to run on a single 4090. Ideal for advanced chatbots, research, and enterprise AI systems.
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🧠 Model Overview: LLaMA 3 70B
- Name: LLaMA 3 70B (Large Language Model Meta AI)
- Release Date: April 18, 2024
- License: Open-weight, but non-commercial
- Use Cases: Chatbots, coding assistants, research, RAG systems, embeddings (with tweaks)
🧬 Architecture
- Parameters: 70 billion
- Layers: 80 transformer blocks
- Hidden Size: 8,192
- Attention Heads: 64
- Feedforward Hidden Size (MLP): 28,672 (i.e., ~3.5x hidden size)
- Vocabulary Size: 128,256 (custom tokenizer)
- Positional Encoding: Rotary Position Embeddings (RoPE)
- Context Length: 8,192 tokens
- Training Tokens: ~15 trillion tokens
- Model Type: Decoder-only transformer (GPT-style)
- Activation Function: SwiGLU
- Attention: Multi-head, with grouped-query attention during inference
⚙️ Training & Infrastructure
- Training Hardware: Custom training using Meta’s Research SuperCluster (RSCC), likely ~24,000 GPUs
- Optimizer: AdamW with linear decay and warm-up
- Precision: Trained in bfloat16 + FP32
- Data Mixture:
- Code
- Academic papers
- Web data
- Books
- Wikipedia
- GitHub
- StackOverflow
- No filtered Common Crawl
- Heavy post-processing + deduplication
💻 System Requirements (for inference)
Running LLaMA 3 70B locally is only feasible on high-end systems:
| Deployment | VRAM Needed | Notes |
|---|---|---|
| FP16 | ~140 GB | Needs 8x A100 80GB or 2x H100 80GB |
| INT4 (GGUF) | ~38–48 GB | Can run on RTX 6000 Ada, 2x 3090s, or Apple M2 Ultra |
| INT8 (GGUF) | ~34–40 GB | Feasible with 1x 4090 (tight) or 2x 3090 |
| CPU (Quantized) | Needs huge RAM (64–128 GB) | Very slow inference |
Popular inference tools:
🚀 Performance Benchmarks
| Task | LLaMA 3 70B | GPT-4 (March ’24) | Claude Opus | Mistral Medium |
|---|---|---|---|---|
| MMLU | 81.7 | ~88 | 86.8 | 73.7 |
| HumanEval (code) | 90.2 | ~89 | ~87 | 67 |
| DROP (QA) | 84.3 | ~87 | 85.5 | 75 |
| ARC-Challenge | 83.2 | 96 | 93 | 76 |
| Winogrande | 86.5 | 89.7 | 87.9 | 80 |
⚠️ Not fine-tuned for function calling or tool use like GPT-4-Turbo, but outperforms GPT-3.5 and Claude Sonnet in almost all benchmarks.
🔐 Quirks & Limitations
- No built-in tools: It’s a raw model. You need to build your own RAG, memory, or agent system.
- No function calling out of the box, but can be coaxed with instruction fine-tuning.
- Can hallucinate if pushed beyond context or domain.
- Doesn’t support fine-tuning yet, but Meta promises tools for it.
🔧 Fine-Tuning & Quantization
- Meta recommends LoRA or QLoRA for parameter-efficient fine-tuning.
- Most people use quantized versions (GGUF format) for:
- Lower RAM
- Mobile inference
- Real-time chatbot use
🧩 Download Options (Community Mirrors)
Meta requires request/approval for full weights via https://ai.meta.com/resources/models-and-libraries/llama-3/
Community options:
- TheBloke on HuggingFace (quantized)
- lmstudio.ai (GUI with local LLM support)
- Oobabooga/text-generation-webui (UI front-end for local LLMs)
📌 TL;DR
| Spec | Value |
|---|---|
| Params | 70B |
| Context | 8,192 tokens |
| Layers | 80 |
| Heads | 64 |
| Training Tokens | ~15T |
| VRAM (FP16) | ~140GB |
| Best Use | High-end chatbots, coding, research |