Best laptops and desktops for programming, AI & data science
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The laptops, desktops and mini PCs in the catalogue Argusic tracks, in one table you can filter by what you will do with it: programming and coding, running AI models on your own machine, or data science. Filter by memory, processor, screen, weight and operating system, then read the buying guide below the table for what each choice changes.
Nothing here was tested hands-on. Specs are what the seller states, memory bandwidth is the maker's, and the largest AI model a machine can hold is marked Estimated, by the method on the methodology page.
Right now: 459 laptops and 487 desktops and mini PCs, each with a price read from the stores we track in the last 24 hours, 935 in stock. Latest price read 2026-09-26 04:22 UTC.
Listing 459 laptops priced from $700, each with a price read in the last 24 hours. Colours of one machine share a row; configurations with different memory do not.
Use forThe rules
Programming & coding: at least 16 GB of system memory and 512 GB of storage, and a processor above the entry class: not an Intel Celeron, Pentium or N series (N95, N100, N150, N200 and similar), AMD Athlon or MediaTek chip. ChromeOS machines are left out. The processor has to be stated.
AI: at least 8 GB of GPU memory (VRAM), or 16 GB of unified memory on Apple silicon, AMD Ryzen AI Max or NVIDIA GB10.
Data science: at least 32 GB of system memory. A GPU is not required; machines with an NVIDIA GPU are marked.
A machine whose listing does not state what a rule needs is not counted as meeting it. Ticking two uses shows machines that meet both.
20 shown of 459 matching
Recommended: $2,000 to $3,000 first, best sellers first, one brand at a time. This is Argusic's editorial default, not a quality ranking. Commissions never change the order.
24 GB RAM1 TB storage16.2 in2.14 kg (store item data)
Memory for a model24 GB unified memory(stated by the seller)/307 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 14B at Q4 / up to 36 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: no
24 GB of unified memory: a 14B model fits at Q4 (estimated)
24 GB of memory and 1 TB of storage: meets the Programming & coding rule
7% below the store's list price of 2,999.00 USD
No CUDA: tools written only for NVIDIA GPUs will not run
Rent or buyThe cheapest verified cloud GPU that runs the same 14B model size is the RTX A5000 (24 GB) at RunPod, 0.16 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,799.00 USDsold by Amazonin stockprice as of 2026-09-26 04:20 UTC Details
LaptopIntel Core Ultra 9 275HXRTX 5070 Ti LaptopWindows 11 Home
32 GB RAM1 TB storage16 in2.65 kg (store item data)
Memory for a model12 GB VRAM(per GPU spec)/672 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 14B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
12 GB of GPU memory: a 14B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
672 GB/s memory bandwidth (NVIDIA spec): up to 78 tokens/s on a 14B model (estimated upper bound)
Weighs 2.65 kg
Rent or buyThe cheapest verified cloud GPU that runs the same 14B model size is the RTX A5000 (24 GB) at RunPod, 0.16 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,548.48 USDsold by Amazonin stockprice as of 2026-09-26 04:20 UTC Details
Memory for a model8 GB VRAM(stated by the seller)/384 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 7-8B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Sold by a third-party seller (Snow Bell), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,549.00 USDsold by Snow Bellin stockprice as of 2026-09-26 04:21 UTC Details
Memory for a model12 GB VRAM(per GPU spec)/672 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 14B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
12 GB of GPU memory: a 14B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
672 GB/s memory bandwidth (NVIDIA spec): up to 78 tokens/s on a 14B model (estimated upper bound)
Weighs 3.3 kg
Rent or buyThe cheapest verified cloud GPU that runs the same 14B model size is the RTX A5000 (24 GB) at RunPod, 0.16 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,688.02 USDsold by Amazonin stockprice as of 2026-09-26 04:20 UTC Details
LaptopIntel Core Ultra 9 275HXRTX 5070 LaptopWindows 11 Pro
16 GB RAM2 TB storage18 in3.1 kg (store item data)
Memory for a model8 GB VRAM(stated by the seller)/384 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 7-8B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: no
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
16 GB of memory and 2 TB of storage: meets the Programming & coding rule
Weighs 3.1 kg
Sold by a third-party seller (Cardinal Pro Electronics), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,139.00 USDsold by Cardinal Pro Electronicsin stockprice as of 2026-09-26 04:22 UTC Details
Programming & coding: not statedAI: yesData science: yes
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Sold by a third-party seller (NextGen Outlet (an authorized Lenovo Reseller)), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,924.66 USDsold by NextGen Outlet (an authorized Lenovo Reseller)in stockprice as of 2026-09-26 04:22 UTC Details
LaptopAMD Ryzen 9 8940HXRTX 5070 LaptopWindows 11 Home
32 GB RAM2 TB storage16 in2.45 kg (store item data)
Memory for a model8 GB VRAM(stated by the seller)/384 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 7-8B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Sold by a third-party seller (PCOnline US), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,249.00 USDsold by PCOnline USin stockprice as of 2026-09-26 04:21 UTC Details
LaptopIntel Core Ultra 9 275HXRTX 5070 LaptopWindows 11 Pro
32 GB RAM2 TB storage16 in
Memory for a model8 GB VRAM(stated by the seller)/384 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 7-8B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Sold by a third-party seller (Maximus Computers (S/N Recorded)), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,499.00 USDsold by Maximus Computers (S/N Recorded)in stockprice as of 2026-09-26 04:20 UTC Details
LaptopAMD Ryzen 9 8940HXRTX 5070 LaptopWindows 11 Home
32 GB RAM1 TB storage16 in2.45 kg (store item data)
Memory for a model8 GB VRAM(stated by the seller)/384 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 7-8B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Sold by a third-party seller (PCOnline US), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,199.00 USDsold by PCOnline USin stockprice as of 2026-09-26 04:21 UTC Details
Memory for a model16 GB VRAM(stated by the seller)/896 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 14B at Q4 / up to 105 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: yes
16 GB of GPU memory: a 14B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
896 GB/s memory bandwidth (NVIDIA spec): up to 105 tokens/s on a 14B model (estimated upper bound)
Sold by a third-party seller (PCOnline US), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 14B model size is the RTX A5000 (24 GB) at RunPod, 0.16 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,799.00 USDsold by PCOnline USin stockprice as of 2026-09-26 04:21 UTC Details
8 GB of GPU memory: a 7-8B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
32 GB of system memory: room for containers, virtual machines and data in memory
Weighs 2.68 kg
Sold by a third-party seller (Issaquah Highlands Tech), not by the store itself
Rent or buyThe cheapest verified cloud GPU that runs the same 7-8B model size is the RTX 3070 (8 GB) at RunPod, 0.13 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,599.99 USDsold by Issaquah Highlands Techin stockprice as of 2026-09-26 04:20 UTC Details
16 GB RAM1 TB storage16 in2.7 kg (store item data)
Memory for a model12 GB VRAM(per GPU spec)/672 GB/s bandwidth maker's spec
EstimatedLargest model that fits: 14B at Q4 / up to 78 tokens/s (upper bound) how
Programming & coding: yesAI: yesData science: no
12 GB of GPU memory: a 14B model fits at Q4 (estimated)
NVIDIA GPU: runs tools written for CUDA
672 GB/s memory bandwidth (NVIDIA spec): up to 78 tokens/s on a 14B model (estimated upper bound)
Weighs 2.7 kg
Rent or buyThe cheapest verified cloud GPU that runs the same 14B model size is the RTX A5000 (24 GB) at RunPod, 0.16 USD an hour. At 160 hours a month, renting would take over 5 years to pass this price. Before electricity, somewhere to put it, and resale.details
2,199.99 USDsold by Amazonin stockprice as of 2026-09-26 04:20 UTC Details
Writing software asks more of memory and storage than of graphics: an editor, a browser, a local server, containers and a build all share the same RAM. This page's Programming & coding rule is at least 16 GB of system memory and 512 GB of storage, and a processor above the entry class: not an Intel Celeron, Pentium or N series (N95, N100, N150, N200 and similar), AMD Athlon or MediaTek chip. ChromeOS machines are left out. The processor has to be stated.
386 laptops of the 459 in this table meet it, from $719.99 to $12,999.00 at the last price check; half of the priced ones cost $1,809.00 or less. 52 laptops do not state enough to judge. You do not need the dearest machine to write code: past the rule, more money buys a lighter body, a better screen or more memory.
Memory (RAM): 16 GB to start, 32 GB for containers and VMs
16 GB is the floor this page uses. Docker containers, virtual machines, an Android emulator or a large monorepo each hold memory of their own, and 32 GB leaves room for them side by side. Check whether the memory can be upgraded later; if the listing does not say, buy the amount you will need for the life of the machine.
422 laptops in this table have 16 GB or more, 303 of them 32 GB or more and 137 64 GB or more. 15 laptops have less than 16 GB, and 22 do not state it.
Processor: x86 or Arm
For editing and running most code, any current mid-range processor is enough; more cores shorten long compiles and help when many containers run at once. The bigger choice is the instruction set. Intel and AMD chips are x86. Apple M and Snapdragon X chips are Arm. Windows on Arm runs many unmodified x64 apps under emulation and Arm-native apps best, per Microsoft's Windows on Arm documentation; if your work depends on a driver, a VPN client or a container image, check that it has an Arm version first.
By processor family, this table has 144 Intel Core Ultra, 126 other Intel Core, 38 AMD Ryzen AI, 110 other AMD Ryzen, 17 Apple M and 7 Snapdragon X laptops; 17 do not name one the page can read. That is 418 x86 and 24 Arm.
Screen size and keyboard
A 13 or 14 inch laptop is easy to carry; a 15 or 16 inch screen fits two files side by side without an external monitor. This table does not carry screen resolution or keyboard quality yet, so check both on the listing, and try the keyboard if you type all day.
50 laptops in this table have a 13 to 14 inch screen, 317 a 15 to 16 inch screen and 86 a 17 to 18 inch screen; 6 do not state a size.
Weight and battery life
If the laptop travels every day, weight matters more than speed. 33 laptops in this table have a stated weight under 1.5 kg (3.3 lb), 126 weigh 1.5 to 2 kg and 215 more than 2 kg; 85 state none. 290 of the weights come from the store's item data rather than the listing text, and those rows say so.
Battery life is not in the table: each maker measures it its own way, so the hours on listings do not compare across brands. Reviews that run one battery test on every laptop are the fair comparison.
macOS, Windows or Linux
Pick the system your tools need. Building apps for iPhone or Mac needs Xcode, which runs only on macOS (Apple's Xcode page). Windows runs a real Linux environment beside it through WSL, the Windows Subsystem for Linux (Microsoft's WSL documentation), so Linux command-line tools and servers run there too. In the Stack Overflow Developer Survey 2025, 49.5% of respondents named Windows for professional use, 32.9% macOS, 27.7% Ubuntu and 16.8% WSL (more than one answer was allowed).
By stated system, this table has 348 Windows, 18 macOS, 0 ChromeOS and 0 Linux laptops; 93 do not state one.
Storage and ports
512 GB is the storage floor of the rule: toolchains, container images and dependencies add up on a 256 GB drive. 419 laptops in this table have 512 GB or more, 340 of them 1 TB or more; 35 do not state it. Ports are not in the table: if you work at a desk with a monitor, check the maker's spec page for USB-C with display output and HDMI.
Best laptops for AI and local LLMs
For a language model on your own machine, memory decides which model fits and memory bandwidth decides how fast it writes. On a laptop with an NVIDIA or AMD graphics card, the memory that counts is the card's own (VRAM); on Apple silicon and AMD Ryzen AI Max, the processor and graphics share one pool of unified memory, so most of it can hold a model (on a 128 GB Ryzen AI Max machine under Windows, up to 96 GB, per AMD). Open-source runners such as llama.cpp work on all of these.
By Argusic's estimate, a model quantized to Q4_K_M (a 4-bit format described in llama.cpp's quantize notes) with an 8,192-token context needs about 7.1 GB for 8B parameters, about 22.7 GB for 32B and about 46.5 GB for 70B. The method is on the methodology page; every model size and speed on this page carries the Estimated label because none was measured.
181 laptops in this table have room for an 8B model, 32 for a 32B model and 6 for a 70B model. 209 laptops do not state enough to tell and are never counted as able to.
NVIDIA CUDA, Apple silicon or AMD Ryzen AI Max
CUDA, NVIDIA's computing platform, runs only on NVIDIA GPUs (NVIDIA's CUDA page), and many AI tools are built on it: if a tool you need says CUDA, you need NVIDIA. For running a model rather than training one, unified memory is the other route, because it reaches sizes a laptop graphics card does not.
In this table: 236 laptops with an NVIDIA GPU, 18 with Apple silicon and 0 with AMD Ryzen AI Max. The most graphics memory on a laptop here is 32 GB of VRAM; the most unified memory is 128 GB.
When renting a GPU beats buying
If you need a large model for a few hours a month, renting a cloud GPU can cost less than a laptop built for it. Each row that runs a model shows the cheapest verified cloud GPU for the same model size and how many months of 160 hours it takes for renting to reach the laptop's price, before electricity and resale. For 181 of the 181 laptops with that line, it takes more than a year; fewer hours stretch it further. The rent-or-buy calculator does the sum with your own hours. A laptop that meets only the programming rule is enough if the model runs in the cloud.
Best laptops for data science
Data science is memory first. pandas works on data held in memory, and a data set that does not fit needs to be read in pieces or moved to another tool (the pandas guide to scaling to large datasets). This page's Data science rule is at least 32 GB of system memory. A GPU is not required; machines with an NVIDIA GPU are marked.
303 laptops in this table meet it, from $779.99 to $16,999.00; 137 of them have 64 GB or more and 273 have 1 TB of storage or more, which matters once data sets and environments pile up.
A GPU is not required for most of the work: scikit-learn runs on the processor, and only a limited list of its estimators can use a GPU so far (the scikit-learn FAQ). A GPU matters for deep learning and for GPU data libraries such as RAPIDS cuDF, which need an NVIDIA GPU (the RAPIDS install guide). 168 laptops that meet the data science rule have one.
Laptop, desktop or mini PC
Choose a desktop or mini PC when the machine never leaves the desk. A desktop has room for a full-size graphics card and more memory than a laptop carries, and it is easier to upgrade; a mini PC puts laptop-class parts in a small box. A laptop remains the answer when you work in more than one place.
The Desktops tab lists 487 machines: 326 towers, 150 mini PCs and 11 all-in-ones. 75 desktops have room for a 70B model by the estimate, against 6 laptops.
The cheapest desktop or mini PC that meets the programming rule is $1,016.00, against $719.99 for a laptop; the cheapest that meets the AI rule is $1,049.99, against $1,028.99, at the last check.
For local AI, the small machines built around one large unified memory are in that tab: 46 with AMD Ryzen AI Max, 5 with NVIDIA GB10 and 3 Macs.
How this page is built
Prices are read from the stores we track: laptop and desktop prices every hour, mini PC prices once a day. Each price shows the seller and the time it was read. Laptops under $700 and desktops under $1,000 are not listed, and neither is a machine without a price read in the last 24 hours. 946 machines are listed now.
Specs are what each seller states on its listing. Memory bandwidth comes from the maker's own spec page, linked on the row (419 rows are matched to one). Model sizes and speeds are estimates from memory and bandwidth, by the method on the methodology page, and are labelled Estimated.
Argusic has not tested these machines hands-on. The default order, Recommended, puts machines from $2,000 to $3,000 first, then those from $1,500 to $2,000 and from $3,000 to $4,000, then the rest; within each band best sellers come first, by the store's sales rank, and no two machines of one brand sit together while another brand is left in the band. It is an editorial default, not a quality ranking; the other sorts are in the menu. The Use for rules are Argusic's and printed beside the filter, and a machine whose listing does not state what a rule needs is never counted as meeting it. Commissions never change the order or any number.
This build: 2026-09-26 04:22 UTC.
Frequently asked questions (FAQs)
How much RAM do I need for programming?
16 GB is the floor this page uses, with 512 GB of storage; 32 GB leaves room for containers, virtual machines and emulators running at once. 422 laptops in this table have 16 GB or more, 303 of them 32 GB or more.
Is a Mac, Windows or Linux laptop better for coding?
The one your tools need. Apps for iPhone and Mac need Xcode, which runs only on macOS. Windows runs Linux tools through WSL. By stated system, this table has 348 Windows, 18 macOS and 0 Linux laptops. Source: Microsoft's WSL documentation
How much memory do I need to run a local LLM?
The memory the model loads into: the graphics card's own VRAM on a machine with a separate GPU, or the unified memory on Apple silicon, Ryzen AI Max or GB10, less what the system keeps. A model that does not fit in VRAM spills into system memory and slows down. By Argusic's estimate at Q4_K_M with an 8,192-token context: 8B about 7.1 GB, 14B about 10.9 GB, 32B about 22.7 GB, 70B about 46.5 GB.
Does NPU TOPS matter for running AI models locally?
Not for which model fits. That is decided by memory size, and the speed of writing by memory bandwidth, and those are the figures each row shows. This page does not rank by NPU TOPS.
Is a Mac or a laptop with an NVIDIA GPU better for AI?
An NVIDIA GPU if a tool you need is written for CUDA, which runs only on NVIDIA. A Mac if you want one large unified memory for running bigger models; the most on a Mac in this table today is 256 GB. The AI platform filter compares the two. Source: NVIDIA's CUDA page
Do I need a GPU for data science?
Not for most of it: pandas and most of scikit-learn run on the processor, and memory matters more. A GPU matters for deep learning and GPU libraries such as RAPIDS, which need an NVIDIA GPU on Linux or on Windows through WSL2. 168 laptops that meet the data science rule here have an NVIDIA GPU. Source: the RAPIDS install guide
Laptop, desktop, or a rented GPU for AI?
In this table 75 of 487 desktops and mini PCs have room for a 70B model by the estimate, against 6 of 459 laptops. A laptop is the choice when the machine travels. For 181 of the 181 laptops with a rent-or-buy line, renting a cloud GPU for the same model size at 160 hours a month takes more than a year to cost the laptop's price.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon.com at the time of purchase will apply to the purchase of this product.
CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON. THIS CONTENT IS PROVIDED 'AS IS' AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME.
A machine appears when its listing is visible in the catalogue Argusic keeps and its price is at or above the floor of its tab; mini PCs appear when their processor or model memory can be read.
Comments never affect rankings or any number on this page.
