Can Your Mac Run It? LLM RAM Calculator
Check whether a model and quant will fit your Mac unified memory or GPU VRAM, with KV cache math, a max context solver, and a tokens per second estimate.
- Apple Silicon makes only about 75% of unified memory addressable by the GPU by default; the rest is reserved for macOS. This ceiling can be raised with the iogpu.wired_limit_mb sysctl, at some risk to system stability.
Overview
A model fits when its weights, KV cache, and about 1.5 GB overhead stay under roughly 90 percent of your available memory. Weights scale with parameter count times bits per weight. KV cache scales with context length, layers, KV heads, and head dimension. Apple Silicon defaults to about 75 percent of unified memory addressable by the GPU, so a 24 GB Mac has roughly 18 GB to work with.
How it works
- 1Pick a model from the dropdown (12 popular open models, from 7B dense to 70B and mixture-of-experts).
- 2Choose a quantization level, from FP16 down to Q2_K.
- 3Drag the context length slider to the conversation or document length you care about.
- 4Toggle KV cache precision between 16-bit, 8-bit, or 4-bit.
- 5Pick your Mac or GPU from the hardware dropdown.
- 6Read the verdict banner, the weights/KV/overhead breakdown, the max context that fits, and the estimated tokens per second.
Worked example
Llama 3.1 8B at Q4_K_M, 8k context, on an M4 24GB
Weights: 8B params x 4.85 bits per weight / 8 = 4.85 GB. KV cache at 8,192 tokens (32 layers x 8 KV heads x 128 head dim, FP16 cache): 1.07 GB. Overhead: 1.5 GB plus 5 percent of weights = 1.74 GB. Total: 7.67 GB, against an 18 GB budget (24 GB unified memory x 75 percent GPU-addressable). Verdict: fits comfortably, with roughly 87,000 tokens of context still available and an estimated 24.7 tokens per second (120 GB/s bandwidth / 4.85 GB active weights).
Qwen3 30B-A3B (mixture-of-experts) on the same Mac
Memory is sized on the model's full 30.5B total parameters (18.49 GB of weights at Q4_K_M), because every expert stays resident even though only some activate per token. Speed, though, is estimated from just the 3.3B active parameters per token, which is why a 30B-class MoE model can run several times faster than a dense model of the same total size.
Methodology & privacy
Weights are estimated as parameter count times bits-per-weight divided by 8, in decimal GB. KV cache size is 2 (key and value) times layer count times KV-head count times head dimension times context length times the KV-cache precision in bytes, which is why grouped-query attention (fewer KV heads than attention heads) shrinks it so much versus full multi-head attention. A fixed 1.5 GB plus 5 percent of the weights covers runtime, tokenizer, and framework overhead. The budget is your hardware's memory times its GPU-addressable fraction: 0.75 by default on Apple Silicon (the rest is reserved for macOS, adjustable with the iogpu.wired_limit_mb sysctl at some risk to stability), 1.0 on a discrete GPU. A model fits when total use is under 90 percent of that budget, is tight between 90 and 105 percent, and does not fit above that. The max-context figure inverts the KV formula to solve for the largest context that still fits the remaining budget, floored to the nearest 1,000 tokens. Tokens per second is a memory-bandwidth-bound estimate: bandwidth divided by the bytes of weights read per token, using total parameters for dense models and only the active parameters for mixture-of-experts models. This ignores compute-bound prompt processing time and real-world overhead, so treat it as a ceiling, not a guarantee.
- Source: Llama 3.1 8B model card (Hugging Face)
- Source: Qwen3-30B-A3B model card (Hugging Face)
- Source: llama.cpp quantize tool, bits-per-weight table
- Source: Apple M4 chip specifications (memory bandwidth)
- Source: NVIDIA A100 datasheet (memory bandwidth)
- Source: r/LocalLLM: "How much VRAM do I really need?"
- Source: r/LocalLLaMA: Mac RAM-tier buying anxiety thread
This calculator runs entirely in your browser. Every model, quant, and hardware pick stays in this page's memory and is never sent anywhere, there is nothing to upload in the first place.
FAQ
How much VRAM do I need to run a 70B model?
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It depends entirely on quantization. A 70B model at Q4_K_M needs roughly 43 GB just for weights (70B x 4.85 bits / 8), before KV cache and overhead. At FP16 that jumps to about 140 GB. Pick the model and quant in the calculator above to see the exact breakdown for your hardware.
What LLM can my Mac actually run?
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Match your unified memory to a model and quant whose total (weights plus KV cache plus overhead) stays under about 75 percent of that memory, since that is what's addressable by the GPU by default on Apple Silicon. A 24 GB Mac comfortably runs 7 to 14B models at Q4 to Q6, while 32B and up generally need 48 GB or more.
Why can't my Mac use all its RAM for an LLM?
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By default, macOS only makes about 75 percent of unified memory addressable by the GPU, reserving the rest for the OS and other apps. This can be raised with the iogpu.wired_limit_mb sysctl (what tools like Siliv automate), but pushing it too high risks system instability.
How much memory does a large context window actually use?
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KV cache size is 2 times layer count times KV-head count times head dimension times context length times cache precision in bytes. For a model with grouped-query attention (few KV heads), a 128k-token context can still add tens of gigabytes even though the base weights are modest, which is why long context surprises so many people.
How do I convert model parameters to gigabytes of RAM?
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Multiply the parameter count in billions by the quantization's bits-per-weight, then divide by 8. An 8B model at Q4_K_M (4.85 bits per weight) is 8 x 4.85 / 8 = 4.85 GB of weights. Add roughly 1.5 GB plus 5 percent of that for runtime overhead, then add KV cache for your context length.
What does "30B-A3B" mean, and does it need 30B or 3B of memory?
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It is a mixture-of-experts model with 30B total parameters and 3B active per token. Memory (RAM or VRAM) is sized on the full 30B, since every expert has to stay loaded, but generation speed behaves like a 3B model, since only the active experts are read per token.
How fast will a model run on my hardware, in tokens per second?
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As a rough, memory-bandwidth-bound estimate, divide your hardware's memory bandwidth (GB/s) by the bytes of active weights read per token. An 8B model at Q4_K_M (4.85 GB of active weights) on hardware with 120 GB/s of bandwidth works out to about 24.7 tokens per second. Real speed also depends on compute, batching, and software, so treat this as a ceiling.
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