Local LLM: Running Large Language Models on Your Own Infrastructure

TLDR: A local LLM is a language model you run on your own hardware: no API calls, no data leaving your environment. This guide covers what that actually means in practice, why teams choose it (privacy, cost, compliance, latency), how quantization makes it feasible on consumer hardware, which serving stacks fit which workloads, and the one honest limitation every local deployment shares: a hard knowledge cutoff that requires a separate strategy to address.

What Running Locally Actually Means

When you run a local LLM, the model weights live on your machine or your organization's servers. Inference happens in your process, on your GPU or CPU. No prompt leaves your network boundary. You are not subject to rate limits set by a third party, acceptable-use policies that change without warning, or outages at a data center you do not control.

The practical ingredients are: a set of model weights (typically several gigabytes of compressed floating-point numbers), an inference runtime that loads those weights and runs the forward pass, and an API layer that exposes completions to your application. The most common runtimes are llama.cpp, which is a C/C++ implementation with broad hardware support, and vLLM, a Python/CUDA engine built for high-throughput serving. Ollama wraps llama.cpp (and Apple's MLX on Apple Silicon) behind a package manager-style CLI that makes the full stack one command. Models are distributed through Hugging Face and the Ollama model library.

This is meaningfully different from a cloud API call. You accept responsibility for model versioning, hardware procurement, and uptime. In return, you get data residency, predictable marginal cost, and full control over what runs.

Why Teams Choose Local Inference

Data Residency and Compliance

Healthcare, finance, and legal teams often operate under regulations that restrict where sensitive data can travel. HIPAA, GDPR, and various financial data governance frameworks can all create situations where sending text to a third-party API is either prohibited or requires contractual overhead that slows deployment. Local inference eliminates that boundary: the data never leaves your perimeter, so there is nothing to disclose. This is not a hypothetical: it is one of the primary drivers of enterprise interest in open-weight models.

Cost Structure

Cloud LLM APIs charge per token. At low volumes that is fine. At high volumes, particularly for batch workflows, document processing pipelines, or developer tools that generate many completions per session, per-token costs accumulate. Local inference replaces variable token spend with fixed hardware cost and electricity. The crossover point depends on your usage volume and hardware choice, but teams running tens of millions of tokens per day frequently find local inference cheaper.

Latency

Network round-trips add latency. For interactive applications like code completion or chat, even a fast cloud API introduces 100 to 500 milliseconds of overhead before the first token arrives. A local model running on a GPU with a warm cache can start streaming tokens in under 50 milliseconds. For latency-sensitive UX, local wins decisively.

Determinism and Model Pinning

Cloud providers update hosted models without always guaranteeing exact version stability. Local deployment lets you pin to a specific model checkpoint and verify that behavior is consistent across time. This matters for regression testing, auditing, and any workflow where you need to reproduce an output exactly.

Quantization: Making Models Fit

A 7-billion-parameter model stored in 16-bit floating point requires roughly 14 GB of memory. Most developers do not have 14 GB of VRAM available. Quantization is the technique that makes local deployment practical: it reduces the bit width used to represent each weight, trading a small amount of quality for a large reduction in memory footprint.

The dominant format for local deployment is GGUF, developed by the llama.cpp project. GGUF files encode the model weights at a specified quantization level and are portable across supported runtimes. The common quantization levels and their rough memory costs for a 7B model are:

Quant level	Bits per weight	Approx VRAM (7B)	Quality impact
Q8_0	8	~7.5 GB	Negligible
Q5_K_M	5	~5 GB	Very small
Q4_K_M	4	~4.1 GB	Small, usually acceptable
Q3_K_M	3	~3.2 GB	Noticeable on harder tasks
Q2_K	2	~2.5 GB	Significant degradation

For most production use cases, Q4_K_M is a practical default: it fits a 7B model in about 4 GB and produces output that is hard to distinguish from the full-precision version on most tasks. Q8_0 is preferred when you have the VRAM and want to be confident quality is not being compromised. Q2_K and Q3_K_M are useful only for severe memory constraints.

llama.cpp supports quantization from 1.5-bit up to 8-bit. vLLM supports FP8, INT8, GPTQ, AWQ, and GGUF, among others. The choice of quantization level should be validated against your specific task: run a representative sample of your real inputs and check output quality before committing to a quant level in production.

Hardware Sizing

The dominant constraint is memory, not compute speed. The model must fit in your available GPU VRAM (or unified memory on Apple Silicon) to run at useful throughput. Once it fits, a mid-range GPU runs a 7B model at 30 to 50 tokens per second, which is faster than a person reads. CPU inference is viable for development and low-throughput tasks: a modern CPU with 16 GB RAM can run a Q4_K_M 7B model at 5 to 15 tokens per second, which is usable for batch jobs but frustrating for interactive use.

Rough sizing rules for inference-only workloads:

• 8 GB VRAM (e.g., RTX 4060): Q4_K_M 7B to 8B models fit comfortably. Add headroom for context.
• 12-16 GB VRAM (e.g., RTX 4070/4080): Q4 13B to 14B models. Q8 7B models.
• 24 GB VRAM (e.g., RTX 4090, RTX 6000 Ada): Q4 30B-class models or Q8 13B. Enough for serious work.
• Apple M-series with 32 GB unified memory: 30B-class models at good throughput via MLX.
• Multi-GPU or 80 GB HBM (e.g., H100): 70B dense models in full precision, or 671B MoE models with tensor parallelism.

Context length also consumes memory. A 128K-token context with a 7B model in Q4 can add 2 to 8 GB depending on KV cache implementation and batch size. Size conservatively: a model that barely fits will thrash or crash under real load.

Serving Stacks

Ollama: Development and Single-User Serving

Ollama is the fastest path from zero to a running local LLM. Install it, run a model, and you have an OpenAI-compatible REST API on localhost:11434. It handles model downloads, GGUF caching, GPU detection, and context management automatically. Its model library covers every major open-weight family.

curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3.1:8b

Ollama is appropriate for development, single-developer use, and low-concurrency production. It is not designed for high-throughput multi-user serving: it queues requests rather than batching them, so throughput does not scale with concurrent users the way a production serving stack does.

vLLM: Production Serving

vLLM, originating from UC Berkeley's Sky Computing Lab, is the standard for production LLM serving. Its core innovation is PagedAttention, which manages KV cache memory in pages rather than contiguous blocks, dramatically improving GPU memory utilization under concurrent load. vLLM supports continuous batching, speculative decoding, tensor parallelism across multiple GPUs, and over 200 model architectures (You.com docs, 2026-09-04).

uv pip install vllm
vllm serve Qwen/Qwen3-8B --host 0.0.0.0 --port 8000

vLLM exposes an OpenAI-compatible API, so applications that already use OpenAI clients work without modification. For teams going to production with local inference, vLLM is the right choice over Ollama when you need real concurrency.

llama.cpp: Direct Control and Edge Cases

llama.cpp is the underlying engine behind Ollama and many other tools. Using it directly gives you maximum control: you can tune thread count, context size, batch size, and layer offloading. It supports CPU-only inference, GPU acceleration via CUDA and Metal, and CPU+GPU hybrid inference for models larger than your VRAM. Its server mode provides an OpenAI-compatible endpoint.

llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

LM Studio: Desktop and Evaluation

LM Studio provides a desktop GUI for running local models. It uses MLX and llama.cpp under the hood and is useful for evaluating models before committing to a deployment stack. Its Bionic agent mode supports document editing, coding tasks, and voice input. It is not a production serving solution but it is an efficient evaluation environment.

The Honest Limitation: Knowledge Cutoff

Every local model has a training cutoff. Llama 3.1 was trained on data through early 2024. Qwen3 and DeepSeek-V3 have their own cutoffs. None of them know what happened yesterday. This is not a temporary limitation that will be fixed by a better model: it is a structural property of how language models work. The weights encode a snapshot of the world at training time.

For applications that do not need current information (code generation, document editing, classification, summarization of content you provide), this does not matter. For applications that need to answer questions about current events, prices, recent documentation, or anything that changes over time, it is a real problem.

The practical solution is to pair local inference with a real-time data source. The most common pattern is RAG (retrieval-augmented generation): your application retrieves relevant content at query time and passes it as context to the local model. For internal knowledge bases, tools like Meilisearch or a vector database handle retrieval. For live web data, you need a search API.

You.com's Web Search API is built specifically for this pattern: it returns real-time, LLM-ready results that you can inject into your model's context. The free tier at https://api.you.com/mcp?profile=free provides 100 you-search queries per day with no credentials; you-contents, you-answer, and you-research are not available on the free tier. For those tools and higher volume, get an API key from you.com/platform. New accounts receive $100 in complimentary credits. The Python SDK is youdotcom; documentation is at you.com/docs (You.com docs, 2026-09-04).

This is not about replacing local inference: you keep local inference for privacy, cost, and latency. You add a search API for the narrow category of queries that require current information. The combination is more capable than either component alone.

Production Considerations

Running a model is not the same as running a reliable service. A few things that matter at production scale:

Memory management: Model loading consumes VRAM. Context length for concurrent requests consumes additional VRAM dynamically. Set conservative limits and test under realistic load before deploying. vLLM's PagedAttention handles this more gracefully than naive serving approaches.

Model versioning: Treat model weights like application code. Pin to specific versions, keep checksums, and test before upgrading. Behavior changes between model versions can silently break downstream applications.

Monitoring: Track tokens per second, request queue depth, GPU utilization, and VRAM usage. These are the signals that tell you whether your serving capacity matches your load. Unlike cloud APIs, you are responsible for collecting and acting on this data.

Concurrency: A single GPU runs one forward pass at a time (with batching). Under high concurrent load without proper batching, requests queue and latency degrades. vLLM's continuous batching keeps GPU utilization high under concurrent load. Ollama does not batch: it serves one request at a time from the queue.

Choosing a Model

The model landscape moves fast. As of August 2026, the major open-weight families available for local deployment include: Meta's Llama family (Llama 3.1 through Llama 4), Alibaba's Qwen3 series (0.6B through 235B), DeepSeek-V3 (671B MoE, 37B active), Google's Gemma 3 and Gemma 4, and Mistral's lineup including Mistral-Small and the Devstral coding variants. All are available through the Ollama library and on Hugging Face. The specific best model for your workload changes faster than any article can stay current: run your own evaluation on representative inputs rather than trusting generic benchmarks.

A reasonable starting point: pick the largest model that fits your hardware at Q4_K_M with room to spare for context. Test on your actual task. Upgrade hardware or quantization level if quality is insufficient.

Getting Started

The minimal path to a working local LLM:

1. Install Ollama from ollama.com.
2. Run ollama run qwen3:8b or ollama run llama3.1:8b depending on your task.
3. Query the OpenAI-compatible endpoint at http://localhost:11434/v1 from your application.
4. Add a search API connection for queries that need current information.

For production, replace Ollama with vLLM, add monitoring, set concurrency limits, and pin your model version. The architecture stays the same: local inference for privacy and cost control, external retrieval for freshness.