Local LLMs

Local LLM GPU calculator: estimate VRAM before you download a model

Estimate whether a local LLM will fit your GPU by thinking through parameter count, quantization, context length, KV cache, CPU offload, and concurrent requests.

Updated 2026-06-118 min readIntermediate

Open the interactive GPU checker Compare Ollama and LM Studio

Best for

Readers choosing a GPU for local AI
Ollama, LM Studio, vLLM, and local RAG users
Teams estimating private inference capacity
Builders deciding between 7B, 14B, 32B, and larger open-weight models

Not for

Exact benchmarking for every driver, kernel, and model architecture
Enterprise capacity planning without load testing
Cloud GPU price comparison without checking current provider pricing

Comparison

Choose by workflow, not brand

Option	Best for	Strengths	Tradeoffs	Use when
8 GB VRAM	Small local models, coding helpers, lightweight chat, and low context windows	Affordable and useful for learning local model workflows.	Limited headroom for larger models, long context, or multiple loaded models.	You mainly run compact quantized models and can tolerate CPU offload.
12-16 GB VRAM	Practical 7B to 14B class workflows, local RAG tests, and smoother desktop usage	Better balance of cost, speed, and model choice.	Still requires careful context and quantization choices for bigger models.	You want a capable local AI workstation without chasing top-end GPUs.
24 GB+ VRAM	Larger open models, longer context, coding models, and local development servers	More room for KV cache, higher quantization quality, and concurrent experiments.	Higher hardware cost and still not a replacement for real load testing.	You run local AI daily and want fewer memory tradeoffs.

The memory formula that matters

Most people estimate only the model weights. Real local inference also needs context memory, runtime overhead, and sometimes memory for more than one loaded model. That is why a model can download successfully but still fail or slow down at a long context length.

Start with parameter count multiplied by quantization bytes per parameter.
Add KV cache for the context window and expected batch or concurrency level.
Reserve a safety buffer for the runtime, graphics driver, OS, and other apps.

Why context length changes the answer

A short chat and a 64K-token document workflow do not have the same memory profile. Long context can make a model that appears to fit become slow, partially offloaded, or queued.

RAG workflows often need less context than full-document stuffing.
Coding workflows may need larger context because related files and error logs must stay visible.
If latency matters, do not plan around a configuration that barely fits.

How to use the calculator result

Treat the estimate as a triage result, not a final benchmark. If it says no, move down in model size or quantization. If it says borderline, reduce context or accept CPU offload. If it says yes, still test real prompts.

Start smaller, confirm speed, then increase context or model size.
Keep one known-good model installed for troubleshooting.
For production-like use, test with the same prompt length and concurrency you expect.

Decision Rules

A practical checklist

If you have 8 GB VRAM, prioritize compact models, Q4 quantization, and short context.

If you have 12-16 GB VRAM, test 7B to 14B class models before buying more hardware.

If you need long context and multiple users, assume the calculator is only step one and run load tests.

If privacy is the reason for local AI, verify where logs, downloads, and API servers are exposed.

Related Guides

Continue the decision path

Open the interactive GPU checker

Enter model size, quantization, context, and GPU memory to get a practical fit estimate.

Open

Compare Ollama and LM Studio

Choose the right local runtime or desktop workflow before installing models.

Open

Interactive GPU fit checker

Use the site calculator to estimate VRAM fit.

Open

Ollama vs LM Studio

Choose a local runtime or desktop app for your workflow.

Open

RAG chunk size guide

Plan retrieval size so local context windows are not wasted.

Open

Chinese Archive

Aligned deeper reading

Ollama knowledge archive

Chinese local LLM notes around Ollama and private model workflows.

Open

DeepSeek local practice

Chinese tutorials and experiments for local model usage.

Open

Topic Hubs

Explore the wider search cluster

Topic hub

RAG and models

Plan RAG systems, local LLM deployment, model APIs, cloud AI platforms, vector databases, evaluation, observability, rate limits, and cost optimization.

Open

Industry Pages

See this guide in a buyer workflow

Industry page

IT operations AI

Compare AI tools for ITSM, AIOps, SaaS management, LLM observability, gateways, rate limits, fallback routing, enterprise search, knowledge management, and IT governance.

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FAQ

Common questions

How much VRAM do I need for a local LLM?

It depends on model size, quantization, context length, runtime overhead, and concurrency. A smaller quantized model may fit on 8 GB, while larger models and long-context workflows often need 16 GB, 24 GB, or more.

Why does a model fail even when the file size is smaller than my VRAM?

The model file is not the full memory requirement. Inference also needs KV cache, runtime overhead, and memory for context and concurrency.

Should I buy a GPU only for local AI?

Only if local privacy, offline access, speed, or repeated experimentation are valuable enough. For occasional use, hosted APIs can be cheaper and simpler.

Source Links

Primary references used for this guide

Reference

Ollama FAQ

Ollama notes on memory, VRAM, and request queue behavior.

Open

Reference

Ollama GPU documentation

Ollama documentation about GPU scheduling and VRAM data.

Open

Reference

LM Studio

LM Studio overview for running local AI models on your own hardware.

Open

Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

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