What is Local LLM?
Local LLM deployment runs models entirely on-device without cloud API calls, providing privacy, offline capability, and zero marginal cost. Local deployment trades convenience for control and cost savings.
This model optimization and inference term is currently being developed. Detailed content covering implementation approaches, performance tradeoffs, best practices, and deployment considerations will be added soon. For immediate guidance on model optimization strategies, contact Pertama Partners for advisory services.
Local LLM deployment eliminates recurring API costs that can reach $5,000-20,000 monthly for high-volume applications, achieving breakeven on hardware investment within 6-12 months. Data-sensitive industries like legal and healthcare gain regulatory compliance by ensuring client information never leaves organizational infrastructure boundaries. The zero-latency local inference also enables real-time applications where cloud round-trip delays of 200-500ms create unacceptable user experience degradation.
- Complete data privacy (no external API calls).
- Offline capability and no usage costs.
- Requires capable hardware (GPU or M-series Mac).
- Limited to smaller models (7B-70B practical range).
- Tools: Ollama, LM Studio, llama.cpp.
- Tradeoff: convenience vs. control/privacy/cost.
- Verify minimum hardware requirements before procurement: 7B-parameter models need 8GB VRAM, while 70B models require 48GB+ across multiple consumer-grade GPUs.
- Benchmark local model quality against cloud API alternatives on your specific use cases, since local models often underperform by 15-25% on complex reasoning tasks.
- Calculate total cost of ownership including hardware depreciation, electricity, and maintenance staff versus cloud API per-token pricing over a 24-month planning horizon.
- Implement automatic model updates through versioned deployment pipelines to prevent running outdated models that miss critical safety patches and performance improvements.
Common Questions
When should we quantize models?
Quantize for deployment when inference cost or latency is concern and minor quality degradation is acceptable. Test quantized models thoroughly on your use cases. 8-bit quantization typically has minimal impact, 4-bit requires more careful evaluation.
How do we choose inference framework?
Consider model format compatibility, hardware support, performance requirements, and operational preferences. vLLM excels for high-throughput serving, TensorRT-LLM for low latency, Ollama for local deployment simplicity.
More Questions
Batching increases throughput but raises per-request latency. Optimize for throughput in offline batch processing, latency for interactive applications. Continuous batching balances both for variable workloads.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Inference in AI is the process of running a trained model to generate outputs -- such as predictions, text responses, image classifications, or recommendations -- from new input data. It is the production phase of AI where the model delivers value to end users, as opposed to the training phase where the model learns.
Inference is the process of using a trained AI model to make predictions or decisions on new, unseen data in real time, representing the production phase where AI delivers actual business value by processing customer requests, analysing images, generating text, or making recommendations.
Repetition Penalty reduces probability of previously generated tokens to discourage repetitive text, improving output diversity. Repetition penalties are essential for coherent long-form generation.
Stop Sequences are tokens or strings that trigger generation termination when encountered, enabling control over output length and format. Stop sequences are critical for structured generation and chat applications.
Structured Generation constrains model outputs to match specified formats (JSON, XML, grammars) through constrained decoding. Structured generation ensures parseable, valid outputs for integration with systems.
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