7 expert LLM Lambda Inference answers researched from official documentation. Every answer cites authoritative sources you can verify.
Optimal GGUF/llama.cpp quantization (2025): (1) Q4_K_M (4-bit medium quality) - best balance: Llama-2-7B 12.55GB → 3.80GB (70% reduction), fits Lambda 10GB memory, 38.65 tokens/sec (2.17x faster vs unquantized), <2% accuracy drop, (2) Q8_0 (8-bit) - higher quality: 6.67GB, 28.5 t/s, <0.5% loss (use for medical/legal/financial), (3) Q5_K_M (5-bit) - middle ground: 4.78GB, 33.2 t/s, <1% loss.
Model size recommendations (Lambda 10GB limit): (1) 1B-1.5B (TinyLlama, Phi-2) - unquantized fits (~3GB FP16), 60-100 t/s, simple tasks (classification, sentiment), (2) 3B-4B (Mistral-3B, StableLM-3B) - Q4 required (4B → 2.4GB), 40-60 t/s, chat/Q&A, (3) 7B (Llama-2-7B, Mistral-7B) - Q4_K_M essential (3.8GB), 25-40 t/s, best quality, production-ready. 13B+ don't fit (13B Q4 ~7.5GB leaves insufficient runtime memory).
Deployment challenges (2025): (1) 250MB .zip limit - model won't fit, must use container images (ECR): FROM public.ecr.aws/lambda/python:3.12; COPY model.gguf /opt/model/; RUN pip install llama-cpp-python, (2) Cold start overhead - container with 7B Q4 = 8-15s (image pull + model load), mitigation: Provisioned concurrency or /tmp caching, (3) 15-minute timeout - batch inference limited to ~300-500 tokens (practical limit at 25 t/s).
Production patterns (2025): (1) Lightweight classification - TinyLlama 1.1B unquantized, Lambda 3GB, ONNX model in 512MB layer, <3s cold start, $0.05/1K requests, (2) Chat/Q&A - Mistral-7B Q4_K_M, container image 10GB, provisioned concurrency = 2, <1s warm inference, $10/day provisioned + $0.20/1K execution, (3) Batch processing - Lambda + Step Functions Distributed Map, 3B Q4 model processes 10K documents parallel, 40 minutes total, $15 cost.
Alternatives (2025): (1) Bedrock - fully managed Llama-2-13B/70B, Claude 3, no deployment/quantization, $0.001-0.003/1K tokens, zero cold starts (best for production), (2) SageMaker Serverless - up to 6GB models (13B Q4 fits), 2-5s cold start, GPU support, $0.20/hour inference + $0.10/GB-hour idle, (3) ECS Fargate - custom containers, GPU (g4dn instances), long-running, $0.04/vCPU-hour + $0.004/GB-hour.
Benchmarks (Lambda 10GB, Llama-2-7B Q4_K_M, 2025): Cold start 12s (container pull 5s + model load 7s), warm inference 100 tokens in 3-4s (25-30 t/s), cost $0.0017 execution + $0.01 provisioned (if used) = $0.012/inference vs Bedrock $0.0002 (60x cheaper). Lambda cost-effective only for <1000 requests/day batch processing.
Use Lambda for: custom models not in Bedrock, cost-sensitive batch processing (<1000 requests/day), offline inference (no real-time requirement), experimental/R&D workloads. NOT for: real-time chat (<500ms latency - use Bedrock/SageMaker), >13B models, >1000 inferences/day (Bedrock cheaper at scale), GPU-accelerated inference. Lambda works for niche use cases, not production chat/real-time.