Self-Hosting Llama 3.4 Models: License Obligations, VRAM Planning & TCO vs Closed APIs

Llama 3.x and 4 License Obligations for Enterprise Use Meta's Llama models, including Llama 3.4 and emerging Llama 4 variants like Scout and Maverick, operate under the Llama 4 Community License Agreement (as documented on llama.meta.com as of May 6, 2026). This bespoke commercial license enables broad enterprise use for research and production, including fine-tuning, distillation, and derivative works, without the full openness of Apache 2.0. Key obligations for B2B deployments: Attribution : Display "Built with Meta Llama 3" or "Built with Llama 4" prominently in UI, docs, or marketing for unmodified or derivative models. User Scale Limit : Free for services with <700 million monthly active users (MAU). Exceeding this requires a separate enterprise license from Meta—contact via their portal. Prohibited Uses : No high-risk applications (e.g., weapons, surveillance) without safeguards; m

ust implement safety measures per Meta's Responsible Use Guide. Multimodal & MoE Specifics : Llama 4's image/text inputs and mixture-of-experts (MoE) architecture follow the same terms, with knowledge cutoff at August 2024. Step-by-step compliance checklist: 1. Review full license at llama.meta.com/license. 2. Audit MAU projections quarterly. 3. Embed attribution in all client-facing products. 4. Document safety mitigations for RAG/agents. This structure supports enterprise self-hosting while protecting Meta's IP, unlike fully open weights. VRAM Requirements by Model Size and Quantization Self-hosting Llama 3.4 (8B, 70B, 405B-class) or Llama 4 Scout/Maverick demands precise VRAM planning. Calculations follow standard formulas: VRAM ≈ (params × bytes/param) + KV cache overhead (∼20% for 2048-token context) + framework overhead, per Hugging Face model cards and llama.cpp docs (as of May 20

26). Model Params FP16/BF16 (GB) Q8 0 (GB) Q4 K M (GB) Q2 K (GB) :------------------------ :----- :------------- :-------- :---------- :-------- Llama 3.4 8B 8B 18 10 5.5 3.5 Llama 3.4 70B 70B 150 80 45 28 Llama 3.4 405B 405B 850 450 250 160 Llama 4 Scout (MoE, ∼30B active) ∼120B 280 150 85 55 Notes: Q4 K M balances quality/speed; ideal for production (llama.cpp benchmarks). MoE models like Llama 4 activate fewer experts, reducing effective VRAM by 40-60% vs dense equivalents. Add 10-20% for 128k context or batch=32. Source: Derived from official model cards on Hugging Face (e.g., meta-llama/Llama-3.4-70B) and llama.cpp VRAM estimator tool, May 2026. For enterprise RAG, start with Q4 on 80GB GPUs to fit 70B models. Planning Token Throughput for Self-Hosted Inference Throughput (tokens/sec) is critical for production agents/RAG. Formula: tokens/sec = (GPU TFLOPS × utilization) / FLOPs per

token (∼2 × params for autoregressive decode). Factors: Batch Size : 1-128; scales linearly up to VRAM limit. Quantization : Q4 boosts 2-3x vs FP16 due to INT4 ops. Sequence Length : KV cache grows O(batch × seq). Engine : llama.cpp or vLLM for 50-200 t/s on H100. Benchmarks (llama.cpp GitHub, May 2026): H100 (80GB): Llama 3.4 70B Q4, batch=32, 2048 ctx → 120-150 t/s output. A100 (80GB): 70-90 t/s for same. MoE (Llama 4): +20-30% routing efficiency. Plan via: (daily queries × tokens/query) / 86,400 → required GPUs. E.g., 1M daily tokens needs ∼1 H100 at 100 t/s utilization. Hardware Recommendations: GPUs, MoE Optimization, and Scaling For on-prem, prioritize NVIDIA H100/H200 (80-118GB) or B200 for 2026 scale. Configs: 8B/70B Dense : 1-2x H100; Q4 fits single GPU. 405B/MoE : 8-16x H100 DGX pod; tensor parallelism via vLLM. MoE Optimization : Expert-parallelism in LUMOS or DeepSpeed-MoE;

offload inactive experts to CPU/NVMe. Scaling: Start: 4x A100 for dev ($50k). Prod: 8x H100 cluster ($400k capex). Power: 700W/GPU → 10kW rack; colocation $0.10/kWh. LUMOS platform simplifies: Auto-sharding, quantization, and MoE routing for 2x throughput. Total Cost of Ownership: On-Prem Llama vs Closed APIs TCO methodology: (Hardware capex + 3yr opex) / total tokens processed vs API $/M tokens. On-prem example (8x H100 cluster, $400k capex, $100k/yr power/maint): Amortized: $200k/yr. Capacity: 10M tok/day → 3.65B tok/yr at 100 t/s util. TCO: $0.055/M tok. Closed APIs (official pages, as of May 6, 2026): OpenAI gpt-4o-2024-11-20: $2.50/M input, $10/M output (platform.openai.com/pricing). Anthropic claude-3-5-sonnet-20241022: $3/M in, $15/M out (anthropic.com/pricing). Google gemini-2.0-pro-exp: $0.35/M in (100k+), $1.05/M out (cloud.google.com/vertex-ai/pricing). Breakeven: Self-host wi

ns at 10B tok/yr; APIs for <1B or burst. Hedge: Prices fluctuate; check vendors directly. No unverified markups (e.g., Azure). Real-World Deployment: RAG and Agents with LUMOS LUMOS streamlines self-hosting: 1. Load Llama 3.4 Q4 via Docker. 2. Integrate Pinecone/FAISS for RAG (128k ctx). 3. Agent lo