Local LLM memory calculator
Choose a model and hardware to calculate the memory you need.
Your configuration
Narrow models to your hardware, and hardware to your model, using your current settings.
Memory needed
estimated use / available memory
- Model weights
- 5.4 GiB
- Conversation memory
- 0.3 GiB
- Runtime allowance
- 1.0 GiB
- Room left
- 16.3 GiB
1.0 GiB reserved for the system and other apps.
Calculation assumptions
Text inference estimate. Image, audio and video inputs need additional encoder and temporary memory.
Local LLM questions, answered.
Practical next steps, with the technical detail there when you need it.
- Ollama hardware requirements: how much RAM and VRAM do you need?
Plan Ollama RAM and VRAM for 8GB, 12GB, 16GB and 24GB GPUs. Compare worked model-memory examples and check GPU offloading before buying hardware.
- How much Mac memory do you need for local LLMs?
Compare 16GB, 24GB, 32GB and 64GB Mac memory for local LLMs. Budget unified memory, context and macOS headroom with worked model examples.
- Ollama vs LM Studio: which should you use for local AI?
Compare Ollama and LM Studio for local chat, APIs, offline use and Mac runtimes. Choose by workflow, then test the same model and memory settings.
- Why is my local LLM so slow?
Troubleshoot slow Ollama, LM Studio and llama.cpp inference. Separate loading, prompt processing and generation before changing hardware.
- Smaller Q8 or larger Q4: which local model should you run?
Choose between a smaller high-precision LLM and a larger quantized model with a practical test for quality, memory, context and response time.
- Local RAG giving bad answers? Check retrieval first.
Fix local document Q&A with a practical RAG checklist: PDF extraction, chunks, embeddings, hybrid search, reranking and a small evaluation set.
Where this can take you
How context uses memory
See how context length, the KV cache and concurrent requests affect local LLM memory requirements.
Read GuideWhy your local LLM runs out of memory in longer chats
Fix local LLM out-of-memory errors as context grows. Budget the KV cache, reduce concurrency and test cache quantization without losing key information.
Read NewsQwen3.8: long context starts with a memory plan
A practical look at the Qwen3.8 27B catalog entry: stored weights, context settings and what to check before a local run.
Read ReferenceModel explorer
Inspect model sizes, capabilities, source records and memory estimates.
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