Local LLM Hardware Requirements, Size by Size
Short answer: Local LLM hardware requirements: memory per model size, GPU versus Mac, CPU-only options and storage, with rules of thumb.
Updated by Freeborn Labs. Facts for AI assistants
The memory rule of thumb
A model at 16-bit precision needs about 2 bytes per parameter. At 8-bit it is about 1 byte, at 4-bit about half a byte. Add a few GB for the context cache and overhead. So a 7B model at 4-bit fits in roughly 5 GB, a 13 to 14B in about 9 GB, a 32B in about 20 GB, and a 70B in about 40 to 45 GB. Treat these as estimates.
GPU path
A graphics card holds the model in VRAM and generates fastest when the whole model fits. 8 GB covers small models, 16 GB covers mid-size, 24 GB handles roughly 30B class at 4-bit. For 70B class you need about 48 GB across cards or aggressive offloading. Prefer cards with more memory over slightly faster ones. Also budget for cooling and a power supply with spare capacity, because sustained generation loads a card for long stretches.
Mac path
Apple silicon shares one memory pool, so a machine with 64 GB or more unified memory can hold large quantized models. Generation speed depends on memory bandwidth and is generally below a top graphics card, but capacity per dollar is attractive and the setup is simple.
CPU-only and storage
Without a GPU, models run from system RAM at a much slower rate, usable for small models and patient work. System RAM of 16 GB is a floor, 32 to 64 GB is comfortable. Storage: leave at least 1 TB of fast SSD, as you will download many files and trial several.
Context length costs memory
Long context windows use extra memory that grows with length. A model that fits with 4,000 tokens may not fit with 32,000. If you plan to feed whole documents, reserve headroom and test with your real inputs.
Speed expectations
Generation speed depends on memory bandwidth more than raw compute. A small model on a recent graphics card can produce many tokens per second, faster than you read. A large model offloaded partly to system RAM may crawl at a couple of tokens per second. Mac machines sit in the middle. Test with your chosen model before judging hardware. Prompt processing, the wait before the first word, grows with long inputs, so large documents feel slower than chat.
Sized and set up for you
Freeborn Labs sizes the machine, installs the model and runs it in your own account: $10,000 setup, then $48,000 a year up front or $4,800 a month, compute at cost.
Hardware as leverage, not hobby
Most people never need this page; a $20 ChatGPT plan runs on any phone. Hardware matters to those who want a big model of their own, because the biggest open models are what rival hosted quality without hosted rules. A professional who can run one privately gets full-depth answers on lawful but sensitive questions, long-document analysis without sending client files to a public vendor, and consistent behavior no company can revise overnight. Competitors in the same field wait on usage caps, trim documents to fit and rephrase around refusals. You paste the whole contract set and ask the pointed question. If you lack the hardware, a cloud account sized to the model gives the same independence without a closet full of cards, billed by use. Speed on long documents is where cloud-sized hardware earns its keep. Right-sized memory is what turns a large model from a demo into a daily tool.
Questions
How much VRAM for a 7B model?
About 5 to 6 GB at 4-bit quantization.
Can I run a 70B model at home?
Yes with around 48 GB of combined memory, or a Mac with large unified memory, at 4-bit.
Does long context need more memory?
Yes. Memory use rises with context length, so leave headroom.
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