{"slug": "llama-3", "title": "Llama 3.", "summary": "Meta's Llama 3.1 70B model can now run on a single 24GB consumer GPU using GGUF or EXL2 quantization, achieving 5-10 tokens per second on an RTX 3090, according to a deployment guide. The guide recommends Ollama or LM Studio for stability and vLLM with AWQ for higher throughput, noting that context filling can cause VRAM spikes and system RAM swapping, dropping speed to 1 token per second.", "body_md": "# Llama 3.\n\n## The VRAM Struggle and the Fix\n\nThe main hurdle with these larger models is the sheer memory footprint. At full precision, a 70B model is a non-starter for home hardware. However, using GGUF or EXL2 formats allows the model to fit within 24GB of VRAM with a small amount of headroom for the KV cache.\n\nIf you're trying to deploy this locally, the most stable way to handle it right now is via Ollama or LM Studio. For those who prefer a more manual AI workflow, using vLLM with AWQ quantization is the way to go for better throughput.\n\n## Deployment Steps for 24GB GPUs\n\nIf you want to get this running from scratch, follow this basic setup to ensure you don't hit an \"Out of Memory\" (OOM) error immediately:\n\n1. Install the runtime (Ollama is the fastest path for beginners):\n\n```\ncurl -fsSL https://ollama.com/install.sh | sh\n```\n\n2. Pull the quantized version of the model. Make sure you aren't accidentally pulling the full-weight version:\n\n```\nollama run llama3.1:70b-instruct-q4_K_M\n```\n\n3. If you are using a Python-based deployment, ensure your `config.json`\n\nreflects the quantized weights to prevent the system from trying to load the model into system RAM, which will tank your tokens-per-second.\n\n## Real-World Performance Benchmarks\n\nI ran a few complex reasoning tasks and a coding script to see if the quantization ruined the logic. Here is how it stacked up:\n\n**Inference Speed:** Roughly 5-10 tokens per second on a 3090. It's not instant, but it's usable for asynchronous tasks.**Logic Retention:** The 4-bit version handles nested if-else logic and complex prompt engineering instructions almost identically to the 8-bit version.**Context Window:** This is where it gets tricky. As the context fills up, VRAM usage spikes. If you hit 24GB, the system will swap to system RAM, and your speed will drop to about 1 token per second.\n\nThe biggest win here is the accessibility. We've moved from needing a server rack to just needing one high-end consumer GPU to run a model that can actually reason. It transforms the local LLM agent from a toy into a legitimate productivity tool for developers who can't send their data to a cloud API.\n\n[Next Four RTX 3060s can actually push 100 tok/s prompt processing on →](/en/threads/6815/)", "url": "https://wpnews.pro/news/llama-3", "canonical_source": "https://promptcube3.com/en/threads/6858/", "published_at": "2026-08-18 22:34:23+00:00", "updated_at": "2026-08-18 22:41:52.150595+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["Meta", "Llama 3.1 70B", "Ollama", "LM Studio", "vLLM", "AWQ", "RTX 3090"], "alternates": {"html": "https://wpnews.pro/news/llama-3", "markdown": "https://wpnews.pro/news/llama-3.md", "text": "https://wpnews.pro/news/llama-3.txt", "jsonld": "https://wpnews.pro/news/llama-3.jsonld"}}