Which LLM can my PC run?

It depends on your GPU's VRAM and memory bandwidth. As a rule of thumb, an 8 GB card runs 7–8B models at Q4, 12 GB runs up to ~14B, 16–24 GB runs 24–32B, and 48 GB+ (or Apple Silicon unified memory) runs 70B models. The Local AI Advisor detects your exact hardware and lists every model that fits, how fast it runs, and the best quantization to use.

How much VRAM do I need to run an LLM?

A rough rule: model VRAM ≈ parameters (in billions) × bits-per-weight ÷ 8, plus the KV cache for your context and ~1 GB of overhead. An 8B model at Q4_K_M needs about 4.8 GB of weights, so ~6–7 GB total — comfortable on an 8 GB card. A 70B model at Q4 needs roughly 40 GB. The advisor computes the exact footprint for any model, quant and context length.

What GPU do I need to run a 70B model locally?

A 70B model at Q4_K_M needs roughly 40 GB of memory, so a 48 GB card (like an RTX A6000), two 24 GB GPUs, or Apple Silicon with 64 GB+ of unified memory can run it fully in memory. On a 24 GB card it only runs by offloading part of the model to system RAM, which drops speed sharply — a 24–32B model is the sweet spot for a single 24 GB GPU.

How fast will an LLM run on my hardware (tokens per second)?

Single-stream text generation is memory-bandwidth bound: each token reads the active weights once, so tokens/sec ≈ usable memory bandwidth ÷ active-weight bytes. For example an RTX 4090 (~1008 GB/s) runs an 8B model at Q4 around 120+ tokens/sec — far faster than reading speed. The advisor estimates realistic tokens/sec for every GPU, model and quantization.

Do I need a GPU to run local AI?

No — you can run smaller models on the CPU alone, but it is much slower because system RAM bandwidth is far lower than GPU VRAM bandwidth. A dedicated GPU, or Apple Silicon with its fast unified memory, is strongly recommended for anything above ~3B parameters if you want a usable, interactive speed.

Which quantization should I use?

Q4_K_M is the default sweet spot — about 97.5% of full quality at ~4.8 bits per weight. Use Q5_K_M or Q6_K for near-lossless output if the model still fits in memory, and only drop to Q3, IQ3 or Q2 when you need to squeeze a larger model into limited VRAM.

Is Ollama or LM Studio faster for local LLMs?

Throughput is nearly identical — both run llama.cpp / GGUF under the hood, so speed is set by your hardware and quantization, not the app. Choose Ollama for CLI and automation, LM Studio for a friendly GUI (and its MLX backend on Apple Silicon), or vLLM / TabbyAPI when you need to serve many concurrent requests.

Can my laptop run local LLMs?

Yes. A laptop with a 6–8 GB dedicated GPU runs 7–8B models comfortably, and Apple Silicon MacBooks with 16 GB+ of unified memory run 8–14B models well (larger models with more memory). The advisor detects laptop GPUs and integrated graphics, and estimates the slower CPU-class speed when there is no dedicated GPU.

Why do Mixture-of-Experts models like Qwen3-30B-A3B feel so fast?

MoE models only activate a fraction of their parameters per token — Qwen3-30B-A3B activates about 3B of its 30B parameters. Because generation speed depends on active-weight bytes, it decodes roughly as fast as a 3B model while answering with 30B-scale quality, which is why it feels much quicker than a dense 32B model.

What AI models can the Apple M3 Ultra run?

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The Apple M3 Ultra pairs 96GB of unified memory with 819 GB/s of bandwidth — and bandwidth is what sets local LLM speed. Here's exactly what it can run, how fast, and at which quantization. Every number below is computed, not guessed.

At a glance

Unified memory

96GB

Bandwidth

819GB/s

Runnable models

15/ 15

Largest model

70BIQ3_M

Top picks for this card

Best for coding

Codestral 22B

Q5_K_M · 40 tok/s · 18 GB

Best for reasoning

DeepSeek-R1-Distill 32B

IQ3_M · 42.1 tok/s · 17 GB

Best for chat

Qwen3 30B-A3B (MoE)

FP16 / BF16 · 100.1 tok/s · 63 GB

Every model on the Apple M3 Ultra

Model	Fit	Best quant	Memory	Speed
Llama 3.2 3B 3.2B · Llama	Perfect	FP16 / BF16	7.8 GB	99.5 tok/s
Qwen3 4B 4B · Qwen	Perfect	FP16 / BF16	9.4 GB	79.5 tok/s
Mistral 7B v0.3 7.2B · Mistral	Perfect	FP16 / BF16	16 GB	45 tok/s
Qwen2.5-Coder 7B 7B · Qwen	Perfect	FP16 / BF16	15 GB	46.4 tok/s
Llama 3.1 8B 8B · Llama	Perfect	FP16 / BF16	18 GB	40.6 tok/s
Qwen3 8B 8B · Qwen	Perfect	FP16 / BF16	18 GB	40.5 tok/s
Gemma 3 12B 12B · Gemma	Perfect	Q8_0	15 GB	49.2 tok/s
Phi-4 14B 14B · Phi	Perfect	Q8_0	17 GB	42.8 tok/s
Qwen3 14B 14B · Qwen	Perfect	Q8_0	17 GB	42.8 tok/s
DeepSeek-R1-Distill 14B 14B · DeepSeek	Perfect	Q8_0	17 GB	42.6 tok/s
Mistral Small 3 24B 24B · Mistral	Perfect	Q4_K_M	17 GB	43.2 tok/s
Gemma 3 27B 27B · Gemma	Perfect	IQ4_XS	17 GB	42.9 tok/s
Qwen3 30B-A3B (MoE) 30.5B · Qwen	Perfect	FP16 / BF16	63 GB	100.1 tok/s
Qwen3 32B 32B · Qwen	Perfect	IQ3_M	17 GB	42.1 tok/s
Llama 3.3 70B 70B · Llama	Recommended	IQ3_M	36 GB	19.4 tok/s

Fit and speed assume a 8K context on 96GB unified memory with 32GB system RAM for overflow. Tokens/sec is single-stream decode. Click any model for its full hardware requirements.

Community-measured speeds

Model	Estimated	Measured (median)	Reports
Qwen3 32B	42.1 tok/s	32 tok/s	1
Llama 3.3 70B	19.4 tok/s	14 tok/s	1

Real generation speeds reported by the community (median, with statistical outliers trimmed), shown against our estimate to keep the model honest. Run the Apple M3 Ultra? Add your own numbers in the advisor.

Frequently asked

How many local LLMs can the Apple M3 Ultra run?

15 of the 15 models we test fit on the Apple M3 Ultra's 96GB of unified memory at a sensible quantization. The largest is Llama 3.3 70B (70B) at IQ3_M, generating about 19.4 tokens/sec.

How fast is the Apple M3 Ultra for local AI?

Single-stream generation is memory-bandwidth bound, and the Apple M3 Ultra has 819 GB/s. On an 8B model at Q4 that translates to roughly 40.5 tokens/sec — comfortably faster than reading speed.

What should I run the Apple M3 Ultra with?

LM Studio with the MLX backend is fastest on Apple Silicon; Ollama and llama.cpp (Metal) also work great.

Can the Apple M3 Ultra run 70B models?

Yes — a 70B model runs, but it fits comfortably in memory.