Self-Hosting Llama 3 Open Weights: Licenses, VRAM Planning & TCO Beats for Enterprises

Llama 3.x/4 License Obligations for Production Use Meta's Llama 3 family—including Llama 3.1, 3.2, 3.3, and emerging Llama 4-class models—releases models as "open weights" under a community license, not fully open-source software. This distinction matters for enterprises: you get weights via Hugging Face or Meta's GitHub, but with production constraints. Key excerpts from the Llama 3 LICENSE file (github.com/meta-llama/llama3/blob/main/LICENSE, as of 2026-05-11): Attribution Requirement : "You must prominently display 'Built with Meta Llama 3' (or the applicable model name) on any website, application, or service that incorporates Llama Materials, with a link to llama.meta.com/llama3 if applicable." MAU Cap : "If your product, service, or use case exceeds 700 million monthly active users (MAU), you must request a separate license from Meta prior to continued use. MAU includes unique user

s accessing Llama via your deployment." Prohibited Uses : No illegal activities, hate speech generation, or child exploitation. Modifications allowed, but derivatives inherit the license. For B2B ops, this means internal deployments (e.g., RAG agents) are fine without MAU hits, but customer-facing apps need tracking. Beyond attribution and MAU, no royalties, but training on Llama outputs for closed models may trigger review requests. Always pair with enterprise-grade monitoring to stay compliant—contact legal@meta.com for 700M scale. VRAM Requirements and Sizing for Llama Models Sizing hardware for Llama inference starts with VRAM calcs. Llama 3.x spans 8B to 405B params; Llama 4-class rumors point to similar scales with multimodal extensions. Core Formula (approximate, excludes KV cache): VRAM (GB) ≈ (Parameters in Billions × Bytes per Param × 1.2 overhead) FP16/BF16: 2 bytes/param → 8B

model: 19 GB; 70B: 168 GB; 405B: 972 GB INT8: 1 byte/param → 405B: 486 GB INT4 (Q4 K M): 0.5 bytes/param → 405B: 243 GB (real-world 750 GB total with cache/context per benchmarks) Add KV cache: For 128K context, (layers × heads × head dim × context × precision × 2) GB. E.g., Llama 3.1 405B at 128K needs 500-800 GB extra in FP16. Enterprise sizing: Consumer GPUs (RTX 4090, 24GB): Q4 8B/13B only. DGX/A100 (80GB) : Q4 70B fits 1x; FP16 30B. H100 (80-141GB) : Q2 405B on 8x; aim 4-8x for prod throughput. Use Hugging Face's estimator or llama.cpp for precise fits. Plan 20-50% headroom for orchestration. Token Throughput Planning on Your Hardware Throughput (tokens/sec) dictates ROI. Benchmarks from Hugging Face Text Generation Inference (TGI) and vLLM (as of 2026 Q1): Llama 3.1 70B Q4 on 1x H100 : 150 tokens/sec output (batch=1, 4K ctx); scales to 500+ t/s batched. 405B Q3 on 8x H100 : 40-80

t/s single-user; 200+ concurrent. A100 80GB (70B INT4) : 80-120 t/s vs H100's 2x faster. Factors: Batch size : Linear scaling up to GPU memory limit. Context length : KV cache quadratic hit—limit to 8-32K for ops. Concurrency : vLLM/TGI handle 10-100 users/GPU. Plan: Query volume × avg tokens/in + out. E.g., 1M queries/mo (2K in/500 out) needs 10 t/s sustained → 2x H100 cluster suffices post-optimization. Optimizing Inference: Quantization and Tools Hit parity with closed APIs via quantization and engines: Quantization : GGUF (llama.cpp) or AWQ/GPTQ (vLLM). Q4 K M loses <2% MMLU; Q2 for VRAM squeeze. Tools : vLLM : PagedAttention for 2-3x throughput. TGI (HF) : Dockerized, OpenAI-compatible API. llama.cpp : CPU/GPU hybrid for edge. exllama/v2 : NVIDIA-only speed demon. Benchmark locally: . Expect 80-95% closed-model speed at 1/10th recurring cost. Total Cost of Ownership: Hardware Breakd

own TCO = CapEx (hardware) + OpEx (power, maint) over 3 years. Sample for 70B prod (10 t/s sustained): 2x H100 SXM (80GB) : $60K-80K each (NVIDIA list as of 2026); total CapEx $200K (w/ servers). Power : 700W/GPU × 24/7 × $0.10/kWh = $12K/yr. 3-yr TCO : $300K → $0.004 per 1K tokens (at 1B tok/mo). 405B scales 8-16x. Lease via CoreWeave/Lambda for opex-only ( $2-4/hr/H100). Amortize via utilization 50%. TCO Comparison: Self-Hosted Llama vs Closed APIs Self-hosting wins at scale. Compare per official pricing (as of 2026-05-11): OpenAI gpt-4o-2024-11-20 : Input $2.50/MTok, Output $10/MTok (openai.com/pricing). Anthropic claude-3-5-sonnet-latest : Input $3/MTok, Output $15/MTok (anthropic.com/pricing). Example: 100M tok/mo (50/50 in/out): API (gpt-4o) : $375K/yr (input $125K + output $500K). Self-host 70B : $100K/yr TCO → 3.75x savings. Llama edges: No token inflation, fixed costs, data sove

reignty. APIs volatile (e.g., +20% hikes historical); hedge with hybrid. Workload Self-Host TCO (70B, $/M tok) gpt-4o API ($/M tok blended) --------------- ------------------------------ ------------------------------ 10M tok/mo 0.10 6.25 1B tok/mo 0.004 6.25 (Self-host scales down; cite assumes 70%