' Llama 3.1 8B Instruct - AEUPH Model Hub
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MODEL DEPLOYMENT Llama 3.1 8B Instruct Zero Ops. Infinite Scale.

Deploy Llama 3.1 8B Instruct to RunPod serverless GPUs. Auto-scaling from 0 to 1000+ workers. Pay per second.

16GBVRAM
2sCold Start
$0.00019/sec (RTX 4090)
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Your Deployment Signal
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Deploy Llama 3.1 8B Instruct

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Llama 3.1 8B Instruct

meta-llama/Meta-Llama-3.1-8B-Instruct

LLM
โ‰ฅ16GB VRAM โญ RTX 3090 Recommended

Model Information

Technical specifications and capabilities for Llama 3.1 8B Instruct.

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Capabilities

  • 128K context window
  • Multilingual (8+ languages)
  • Tool calling / function calling
  • Instruction following
  • Code generation & reasoning
  • RAG-ready embeddings
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Performance

  • ~200 tok/s on RTX 3090
  • ~400 tok/s on RTX 4090
  • ~800 tok/s on A100 40GB
  • FP16 / FP8 quantization
  • Flash Attention 2 support
  • vLLM / TGI optimized
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Quantization

  • AWQ 4-bit (recommended)
  • GPTQ 4-bit / 8-bit
  • GGUF Q4_K_M / Q8_0
  • FP8 (H100/A100)
  • FP16 (full precision)
  • Custom calibration
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Use Cases

  • Chat assistants & agents
  • Code generation & review
  • RAG & document QA
  • Content generation
  • Reasoning & analysis
  • Fine-tuning ready
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Native Hugging Face Integration

Deploy any HF model ID directly. Auto-detects architecture, quantization (AWQ, GPTQ, GGUF), and required VRAM. OpenAI-compatible API endpoints. Built-in Flash Attention, vLLM, TGI, and custom Docker support.

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Spot Instances: Save 50-70%

Bid on spare capacity. RTX 3090 at $0.11/hr (vs $0.22), A100 40GB at $0.55/hr (vs $1.10). Perfect for batch inference, fine-tuning, and fault-tolerant workloads. Automatic fallback to on-demand.

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Global Edge & CDN Integration

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