Running AI Models on a Laptop: The 2026 Landscape and Picks

The most important fact about running AI models on a laptop in 2026 is that speed is set by memory bandwidth, not NPU TOPS: LLM decode re-reads the whole model from memory for every token, so tokens per second is roughly bandwidth divided by model size. That's why a headline like "40 TOPS NPU" tells you almost nothing about local LLM speed. A laptop is a different game from a desktop GPU: Apple Silicon's unified memory lets the CPU, GPU, and Neural Engine share one memory pool, so a 32GB Mac loads a 28GB model with no copying - it runs models a discrete GPU can't fit at all. Conversely, a Windows AI PC's NPU is great for small on-device features but wrong for large LLMs. In short: large local goes to the Mac's unified memory; battery and AI features go to a Copilot+ NPU.

In plain terms: laptop local AI is plumbing and a water tank. More than TOPS (pump power), the pipe width (bandwidth) and tank size (memory) decide how fast and how much water comes out. Buy a big-pump NPU with a thin pipe and you get a trickle - on a laptop, look at bandwidth and memory first.

What can a laptop actually run?#

Memory capacity sets model size. At 4-bit, 7B is about 5GB (plus 4-6GB for macOS, so you need 10-11GB free), and 70B is about 40GB, which needs a 48GB+ Mac. Because Apple is unified, it loads into the whole RAM pool, not VRAM - an M5 Pro with 64GB comfortably runs 30-35B at 4-bit. On Windows shared memory, 16GB is the practical floor, 32GB is comfortable, 64GB is for developers. So "how many B can I run" ultimately comes down to your laptop's RAM.

Here is why bandwidth matters so much. Each time an LLM produces one token, the GPU must read the entire model weights from memory once. The math is instant, but hauling tens of gigabytes takes time. That gives the simple rule tokens/sec ~ bandwidth / model size - for example, 273 GB/s with a 5GB model is on the order of 50 tokens/sec in theory (lower in practice due to overhead).

2026 laptop local AI - landscape and picks by type (public benchmark, measured snapshot) · columns: Type/tier, Measured trait, Recommended use · 출처 Hax hax.moche.ai/en/p/1088?ref=ai_answer

2026 laptop local AI - landscape and picks by type (public benchmark, measured snapshot) · columns: Type/tier, Measured trait, Recommended use · 출처 Hax hax.moche.ai/en/p/1088?ref=ai_answer

Type/tier | Measured trait | Recommended use
MacBook Air M5 | ~153 GB/s, unified memory | small-mid (7-14B), value
MacBook Pro M5 Pro 48GB | 273 GB/s | 30B-class serious local
MacBook Pro M5 Max 64GB+ | 546-614 GB/s | only 70B-class laptop
Copilot+ AI PC | NPU 40-85 TOPS | on-device features, battery
Ryzen AI Max+ 395 | 96GB iGPU, ~14 tok/s | Windows 70B exception

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Why are Apple's M5 and MLX a leap?

What about NPUs and long prompts?

So what's the 2026 laptop AI recommendation?

Why are Apple's M5 and MLX a leap?#

Unified-memory-native design plus Neural Accelerators. Apple's own framework MLX is built from scratch for unified memory, avoiding needless copies and automatically using the M5's new Neural Accelerators. By Apple's MLX research, the M5 is 3.5-4x faster to first token (TTFT) than the M4 (1.7B-30B), and token generation is 19-27% faster, capped by bandwidth. Note that from the M5, Apple stopped quoting Neural Engine TOPS, switching to relative speedups, so treat "M5 = N TOPS" as an estimate. For tooling, split Ollama for ecosystem, MLX for peak speed (they converge at 70B, where bandwidth dominates).

Once more on why unified memory is special. Most laptops and desktops keep system RAM and GPU VRAM separate, so sending a model to the GPU requires a copy and VRAM capacity is the ceiling. Apple has the CPU, GPU, and Neural Engine share one RAM pool, so there is no copy and the whole pool (say 64GB) is the model ceiling. That is how a laptop ends up running a model too big for a discrete GPU.

What about NPUs and long prompts?#

NPUs are not for large LLMs, and long inputs favor NVIDIA. Copilot+ certification floors at 40 TOPS, 16GB, 256GB, and in 2026 the Snapdragon X2 Elite leads at 80-85 TOPS (up 78% from last year's 45-48), but the NPU is not the path to running 70B - it's for small sustained on-device models and Windows AI features. And long-prompt processing (prefill) is compute-bound, so a 128K-token input takes several minutes on an M5 Max but seconds on a high-TOPS NVIDIA card. In other words, bandwidth sets "how fast you read the answer," compute sets "how fast it reads your question."

So what's the 2026 laptop AI recommendation?#

The key is decide the laptop type first, then choose by bandwidth and memory, not TOPS.

Large local: the Mac's unified memory (M5 Pro 48GB = 30B-class, M5 Max 64GB+ = the only 70B laptop), with MLX for speed.

Battery and AI features: a Copilot+ AI PC (on-device features, all-day battery); for future-proofing aim 50+ TOPS and 32GB.

Verify: measure on your own workload - in tok/s and TTFT, not benchmark TOPS. A DRAM shortage trimmed high-capacity tiers in 2026, so check current specs before buying.

Related reading: ComfyUI로 이미지·영상 만들기: 우리가 직접 굴리며 잰 운영 회고, 오픈 음성 클로닝 파이프라인 — 우리는 이렇게 운영한다

Related reading: 노트북에서 돌리는 AI 모델 VRAM·RAM 요구량 실측, 노트북에서 돌리는 AI, 5분 시작 가이드(초보자용)

Reference links

MLX (Apple Silicon ML framework)

llama.cpp (Mac/CPU local inference)

Ollama (local model runner)

Apple ML Research (MLX, on-device)

Microsoft Copilot+ PC (requirements)

Note: figures like M5 bandwidth (120/273/546-614 GB/s), TTFT 3.5-4x, and NPU 80-85 TOPS are 2026 public and vendor-reported numbers that vary by configuration and test conditions (not permanent; e.g., the M5 Max is cited at both 546 and 614). From the M5, Apple no longer states NPU TOPS, so that number is an estimate. A DRAM shortage shifted memory ceilings and prices in 2026, so check current specs before buying. Laptop silicon moves fast, so this is reviewed quarterly.