How to Run LLMs Locally in 2026: The Complete Guide

Running an LLM on your own hardware in 2026 is a solved problem — the hard part is picking the right tool for who you are. "Local" spans four very different projects: Ollama and LM Studio for the one-command / one-click desktop path, llama.cpp for the dependency-free engine underneath much of it, and vLLM for high-throughput GPU serving to many users at once. This guide maps each to its job, tells you what VRAM and RAM a given model size actually needs (and where those numbers come from), how quantization changes the math, and — honestly — the point where a hosted gateway is cheaper and less trouble than the GPU under your desk.

The short answer: pick by who you are

There is no single "run LLMs locally" tool — there are four, and the right one is decided by whether you want a desktop app, a scriptable engine, or a multi-user server. In one line each, straight from each project's own description:

• Ollama — "the easiest way to build with open models," a one-command local runner with an optional cloud tier for models too big for your hardware. (ollama.com)
• LM Studio — "Run AI models, locally and privately," a desktop GUI (plus headless CLI and an OpenAI-compatible API), free for home and work. (lmstudio.ai)
• llama.cpp — a dependency-free C/C++ inference engine built for "minimal setup and state-of-the-art performance on a wide range of hardware," and the format (GGUF) and quantization work much of the ecosystem sits on. (github.com/ggml-org/llama.cpp)
• vLLM — "a fast and easy-to-use library for LLM inference and serving" from UC Berkeley's Sky Computing Lab, built for throughput when many users hit the same GPUs. (github.com/vllm-project/vllm)

If you just want a model on your laptop today, jump to Ollama & LM Studio. If you are serving an app to real users, jump to vLLM. Either way, read the hardware section before you download a 70B model you cannot load.

The tool picker: Ollama vs llama.cpp vs LM Studio vs vLLM

All four run open models locally; they differ on interface, audience and whether they are built for one user or many. This table is our synthesis of each project's own primary-source positioning (linked in the row footnote) — not a benchmark:

Sources, per row: Ollama GPU docs; LM Studio lmstudio.ai; llama.cpp README; vLLM README. All verified July 2026. "Best for" is our editorial read of each project's stated purpose, not a ranking.

The easiest path: Ollama & LM Studio

For a single machine, these two hide every hard part — model download, GPU offload, quantization — behind one command or one click.

Ollama calls itself "the easiest way to build with open models" and runs them locally on your own hardware, with an optional cloud tier for models that need datacenter-grade GPUs. A first run is one line:

# install, then pull and chat with a model
ollama run gpt-oss:120b

# or a small model that fits a laptop
ollama run llama3.1:8b

On hardware, Ollama's official GPU docs are specific about drivers rather than model sizes: it supports NVIDIA GPUs with compute capability 5.0+ on driver 550+ (GPUs at CC 5.0–6.2 need driver 570+), AMD GPUs via the ROCm v7 driver stack, Apple Silicon via Metal, and additional Vulkan-based GPU support on Windows and Linux. Notably, those docs do not publish fixed per-model VRAM minimums — the scheduler reads available VRAM from your GPU libraries at runtime and places layers accordingly. Any "model size → RAM" table you see comes from third-party guides, not Ollama itself (more on that below).

LM Studio is the graphical counterpart — tagline "Run AI models, locally and privately," free for home and work, and able to run fully offline. Beyond the chat GUI it ships a headless CLI, a JavaScript SDK, a Python SDK and an OpenAI-compatible API, and its site names support for open families like gpt-oss, Qwen, Gemma and DeepSeek-R1. That OpenAI-compatible endpoint matters: it means an app written against the OpenAI SDK can point at LM Studio locally with only a base-URL change — the same portability trick a hosted gateway uses.

The engine: llama.cpp and GGUF

llama.cpp is the low-level engine a lot of the "easy" tools are built on — reach for it directly when you want maximum control or to embed inference in your own C/C++ app. Its stated goal is "to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware — locally and in the cloud," using a plain C/C++ implementation with no dependencies.

Two things make it the backbone of the local scene:

• GGUF, the format. llama.cpp uses the GGUF model format (other formats convert via its convert_*.py scripts). When you download a .gguf file from a model hub, this is the lineage you are in.
• Quantization, from 1.5-bit to 8-bit. It supports integer quantization across that whole range for faster inference and lower memory, and — crucially for anyone short on VRAM — CPU + GPU hybrid inference, letting you run models larger than your GPU's VRAM by spilling layers to system RAM. Hardware coverage is broad: CPU (ARM NEON/Accelerate/Metal on Apple Silicon; AVX/AVX2/AVX512/AMX on x86) and GPU (CUDA, HIP, Vulkan, SYCL).

The practical upshot: if a model "just works" in Ollama or LM Studio, this is usually why. You only drop down to llama.cpp directly when you need a build flag, a specific quant, or to link the engine into your own binary.

Serving at scale: vLLM

vLLM is the odd one out — it is not a desktop app, it is a production serving engine for when many requests hit the same GPUs. It describes itself as "a fast and easy-to-use library for LLM inference and serving," originally developed at UC Berkeley's Sky Computing Lab, delivering "state-of-the-art serving throughput."

What earns it that label is a set of serving-specific techniques rather than a nicer UI:

• PagedAttention for efficient management of attention key/value memory.
• Continuous batching of incoming requests, plus chunked prefill and prefix caching.
• An OpenAI-compatible API server (plus an Anthropic Messages API and gRPC), so it drops into existing clients.

It supports many quantization formats (FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF), runs on NVIDIA and AMD GPUs and x86/ARM/PowerPC CPUs with plugins for Google TPUs, Intel Gaudi and Apple Silicon, and covers 200+ model architectures. In short: vLLM is what you run when the local model becomes a shared service. If you are building an agent backend that many users call at once, this is the tier you graduate into — and at that point the build-vs-buy question against a managed gateway gets real (see the cost section).

Hardware: how much VRAM you actually need

The honest answer is a function of parameter count and quantization, and the most authoritative number is simple: a quantized model needs roughly as much memory as its file size. That is straight from llama.cpp's official quantize docs — models are fully loaded into memory, so RAM needs roughly match the (quantized) file size, and disk and memory requirements are the same. Their own Q4_K_M examples:

Source: llama.cpp quantize README (verified July 2026). "≈ RAM" because the file loads fully into memory; add headroom for context/KV cache on top.

The widely-repeated "7B needs 8GB, 13B needs 16GB, 70B needs 64GB" rules of thumb are community guidance, not official — one third-party tuning guide suggests roughly ~8GB RAM / 4–8GB VRAM for 7B, ~16GB / 8–16GB for 13B, and ~64GB / 24GB+ for 70B. Treat those as approximate: they move with quantization level and context length. And remember Ollama's own docs deliberately publish no fixed per-model minimums — its scheduler decides placement from the VRAM it sees at runtime.

What about the truly large models? The jamesob/local-llm project documents a hardware-first approach using vLLM docker-compose runners, and it illustrates the scaling clearly: heavier local setups grow with VRAM, from a dual-GPU (2× 24GB-class) tier for mid-size models up to multi-GPU builds with hundreds of GB of VRAM for the biggest ones. That is one author's build, not a general benchmark — treat any specific throughput or dollar figures there as their setup, not a spec. For the concrete case of the largest open-weight model most people care about, we have a dedicated breakdown in GPT-OSS 120B hardware requirements.

Picking a quantization

Quantization is the single biggest lever between "fits my GPU" and "does not" — and 4-bit is the usual sweet spot. Per llama.cpp's docs, quantization reduces weight precision (e.g. 32-bit floats down to 4-bit integers), shrinking the model and speeding inference, but "may introduce some accuracy loss" — measured via perplexity or KL divergence — which can be minimized with a suitable imatrix file. The engine supports the full 1.5-bit to 8-bit integer range.

How to choose, in plain terms:

• Q4_K_M (4-bit) — the default most people should start on: big memory savings, small quality cost. This is the size in the table above.
• Q5 / Q6 / Q8 (higher-bit) — closer to full quality, larger files; use when you have VRAM to spare and care about the last few points of accuracy.
• Sub-4-bit (down to 1.5-bit) — only when memory forces it. Accuracy loss grows as bits fall, so this is a last resort to squeeze a model onto limited hardware, not a free lunch.

Two guardrails. First, budget extra memory beyond the weight file for the KV cache, which grows with context length — a long context can add several GB on top of the numbers above. Second, quality-per-bit varies by model, so if a specific quant of a model feels "off," try one notch higher before blaming the model. For a worked example on a specific model, see how to run DeepSeek locally.

When the API or gateway is cheaper

Local inference is free per token — but only after you have paid for the GPU, the power, and the idle time between requests. Below a real volume threshold, a hosted API wins on total cost. The honest trade-off, without the marketing on either side:

A decision note, not a benchmark: which side wins depends on your sustained tokens/day, your privacy requirements, and whether you already own a capable GPU. There is no universal break-even — model it against your own volume.

The rule of thumb that falls out of that table: self-host when you have steady, high-volume, privacy-sensitive, or offline workloads and already own (or can justify) the GPU; use an API when your traffic is bursty, low-volume, or spiky, or when you need a frontier-scale model no single desk can hold. Many teams do both — run a small model locally for the common case and route large or overflow requests to a hosted endpoint. That hybrid is exactly what an LLM gateway makes clean: one OpenAI-compatible base URL, with routing and failover deciding per request whether to serve locally or in the cloud. For the pure cost comparison across hosted providers, see our cheapest LLM API in 2026 guide.

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