What is the most important hardware spec for running LLMs locally?

VRAM. The model's weights plus its KV cache must fit in GPU memory to run at usable speed. Memory bandwidth then determines how fast it generates tokens once it fits. Raw compute (FLOPS) matters least for single-user inference. Size your VRAM to the largest model you intend to run, then optimize for bandwidth.

How much should I spend to run LLMs locally?

It depends entirely on model size. A used RTX 3090 (around $900) handles 7B–34B models comfortably and is the best value entry point. Running 70B models well needs roughly 48GB of VRAM — a pair of used 3090s or a large-memory Mac. See the budget and cheapest-way guides linked above for tiered options.

Should I buy a GPU or just rent one in the cloud?

Rent if your usage is occasional or bursty, or if you need datacenter-class hardware briefly. Buy if you run a model many hours a day on a steady basis, or if your data must never leave your machine. The break-even is mostly a function of how many hours per week the card is actually busy — run your numbers through the cloud-vs-buy calculator.

Explainer

Local LLM Hardware: The Complete Guide (2026)

June 16, 20269 min readBy the ClankerBuilder editorial team · how we rate

The complete guide to choosing hardware for running LLMs locally — GPU VRAM, quantization, Mac vs PC, cost vs cloud, and which build fits your use case.

Running large language models on your own hardware comes down to one decision made well: matching VRAM, speed, and budget to the models you actually intend to run. This guide is the map. It walks the whole decision — how much VRAM a model needs, what quantization buys you, whether a GPU or a Mac fits your goals, and when renting beats buying — and links to the in-depth article for each step.

If you read nothing else: pick the largest model you genuinely plan to run, add up its weights plus KV cache, and buy the cheapest hardware whose memory clears that total with headroom. Everything below is the detail behind that one sentence.

Start with VRAM and quantization

Memory is the gate. A model only runs fast if its weights and KV cache fit in VRAM; the moment it spills to system RAM, throughput collapses. Quantization is the lever that decides how much memory a given model needs, trading a little quality for a lot of capacity.

Start here: How Much VRAM Do You Need to Run Llama 70B? for the sizing math, LLM Quantization Explained: Q4 vs Q8 for how precision changes the numbers, and Tokens Per Second Explained for what 'fast enough' actually means. For a model-specific worked example, What Hardware Do You Need to Run DeepSeek Locally? applies this sizing to DeepSeek's MoE and distilled variants.

Choosing a GPU

For most people the right answer is a single 24GB-class NVIDIA card, with the used RTX 3090 as the value champion. Which card, and whether you ever need two, depends on the models you target.

Read Best GPU for Running Local LLMs for the tier-by-tier picks, RTX 4090 vs 3090 for AI for the speed-vs-price tradeoff at 24GB, and Single vs Dual GPU for LLM Inference for when pooling VRAM across two cards is worth the complexity. Considering the current-gen card or a non-NVIDIA option? See Is the RTX 5090 Worth It for Local LLMs? and AMD vs NVIDIA for Local AI.

Mac, or PC?

Apple Silicon's unified memory lets a single quiet machine hold models that would need a multi-GPU PC, at the cost of raw speed and a weaker fine-tuning story. NVIDIA wins on tokens-per-second and the CUDA ecosystem.

Compare them in Mac vs PC for Local AI, and if you lean Apple, Best Mac for Running LLMs Locally breaks down the memory tiers. Need something portable? Best Laptop for Running LLMs Locally covers the laptop tradeoffs.

Budget paths and the cheapest way in

You do not need to spend $4,000 to start. There are honest budget paths at every tier, and a cheapest-possible route if you accept the tradeoffs.

See Best Budget AI Workstation Builds for 2026 for tiered builds and The Cheapest Way to Run a 70B Model Locally for the rock-bottom option and why the value sweet spot sits one rung up. New to all of this? How to Run Llama 3 Locally is the gentlest on-ramp, and Ollama vs LM Studio helps you pick the software to run models with.

Buy or rent?

Owning hardware only pays off past a usage threshold. If your work is bursty or you need a card you could never buy, the cloud wins; if you run a model many hours a day on data you want kept local, buying wins.

Work it out in Cloud GPU vs Buying a Workstation, or plug your real numbers into the cloud-vs-buy calculator.

Going further: fine-tuning, teams, and multi-GPU

Once you are past single-user inference, the requirements change. Training needs far more memory than inference; serving a team needs concurrency headroom; and multi-GPU rigs bring real power and cooling demands.

Dig into What Hardware Do You Need to Fine-Tune an LLM?, Building a Self-Hosted LLM Server for Your Team, and Power and Cooling for Multi-GPU AI Rigs. When you are ready to spec a machine, the build guides and the PC builder turn these decisions into a compatible parts list.

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