Framework Laptop 16: Modular Developer Laptop with Swappable GPU

The Framework Laptop 16 is the first modular laptop to take GPU upgrades seriously. Featuring a swappable graphics module, six configurable port slots, and open-source firmware, the 16-inch device targets developers and cloud engineers who don’t want to replace their tools every two years. Starting at around 1,132 euros as a DIY kit.

Key Facts at a Glance

• Top configuration features the AMD Ryzen AI 9 HX 370 (12 cores, Zen 5 architecture, 45W TDP). No Intel option is available (Framework, 2025).
• The expansion bay system accepts swappable GPU modules-currently the NVIDIA RTX 5070 Laptop (8 GB GDDR7) or AMD Radeon RX 7700S (8 GB GDDR6). Module swaps take under five minutes (Framework).
• Six customizable expansion card slots (USB4, USB-A, HDMI 2.1, DisplayPort 1.4, 2.5GbE Ethernet, up to 1 TB of storage per slot).
• Schematics, CAD files, and embedded controller firmware are open source on GitHub (Framework, github.com/FrameworkComputer).
• Battery life: 8 to 9 hours under light workloads (Tom’s Guide, 2025). Display: 16-inch, 2560×1600 resolution, 165 Hz refresh rate, 100% DCI-P3 color gamut.

The Thesis: Why Modularity Matters to Developers

Developer laptops have an expiration date-not because the CPU becomes too slow after three years, but because the GPU falls short, the ports no longer match a new setup, or the RAM is soldered and non-upgradeable. Framework addresses this not with marketing promises, but with a physically modular design.

The expansion bay system on the rear of the chassis connects via PCIe and accepts swappable modules. If you start today with the AMD Radeon RX 7700S and want to upgrade to the next GPU generation in two years, you simply swap the module-not the entire laptop. Framework has confirmed cross-compatibility between its 2024 and 2025 mainboards.

For cloud engineers testing local AI inference workloads or training ML models, this offers a tangible advantage: GPU performance scales with module replacement, without needing to replace the keyboard, display, or mainboard.

Expansion Cards: The USB-C Building Blocks

Six slots-three on each side. Each Expansion Card uses a USB4/USB-C form factor and can be swapped while the system is running. Available options include USB-C (USB4/USB 3.2), USB-A, HDMI 2.1, DisplayPort 1.4, microSD, SD, 2.5GbE Ethernet, 3.5-mm audio, and storage cards offering up to 1 TB per slot.

For the daily workflow of a DevOps engineer, this means: in the office, two USB-C ports (for docking and peripherals), one HDMI, and one Ethernet connection are used. On the go, Ethernet and HDMI are swapped out for an SD card and USB-A. No dongles, no hubs-the ports are always exactly what you need.

The two rear slots support USB Power Delivery (USB-PD) 3.1 with up to 240 watts of input power-enough to charge the laptop even under full load with a GPU module using only USB-C. Simultaneous display output across four slots is also supported, a configuration that would typically require a Thunderbolt dock on conventional laptops.

GPU Modules: RTX 5070, RX 7700S, or None at All

The expansion bay system currently offers four options:

NVIDIA GeForce RTX 5070 Laptop GPU (around 391 euros): 8 GB GDDR7, 100 W TGP. Ideal for ML inference with small to medium-sized models, CUDA-based development, and GPU-accelerated containerization. The RTX 5070 supports DLSS 4 and is compatible with mainstream ML frameworks (PyTorch, TensorFlow, ONNX Runtime).

AMD Radeon RX 7700S (around 391 euros): 8 GB GDDR6. A capable all-rounder GPU with strong OpenCL performance. On Linux, the open-source drivers (Mesa/RADV) run stably and efficiently. A serious alternative for developers who don’t rely on CUDA.

Dual M.2 Adapter: Two PCIe 4.0 x4 slots instead of a GPU. Designed for users who prioritize maximum local storage or want to install an AI accelerator (e.g., Coral M.2, Hailo-8) in the expansion bay.

Expansion Bay Shell: A fanless blank cover for pure integrated GPU operation. The built-in AMD Radeon 780M (Ryzen AI 9) handles development tasks, external displays, and light GPU workloads adequately.

Open Source: Firmware, Schematics, Community Modules

Framework publishes schematics, CAD files, and firmware on GitHub. The expansion bay interface is fully documented. The embedded controller runs open-source firmware, and the input modules use QMK firmware on a Raspberry Pi RP2040.

The community has seized this opportunity: third-party modules such as an RGB LED matrix display and an M.2-to-PCIe adapter already exist as GitHub projects. For IT teams building internal developer platforms, this openness sends a clear signal-hardware you can understand and control.

More relevant for enterprise use: Framework uses no proprietary screws or adhesives. Every component-mainboard, display, ports, GPU module, keyboard, and trackpad-is replaceable. Framework has publicly committed to long-term spare parts availability, lowering total cost of ownership and making the device attractive for corporate fleets with extended usage cycles.

Linux Compatibility: No Driver Lottery

Framework officially supports Linux and maintains installation guides for Ubuntu 24.04 and Fedora 40. Wi-Fi, Bluetooth, webcam, fingerprint reader, and all Expansion Cards work out of the box-no additional drivers required. Suspend and hibernate function reliably.

The AMD integrated GPU leverages open-source drivers (Mesa/AMDGPU) included in recent kernels. Real-world testing shows Ubuntu 24.04 sometimes outperforming Windows 11 on identical hardware in compute benchmarks. For developers already working in Linux containers and testing microservice stacks locally, dual-boot setups become unnecessary.

One often overlooked advantage: the NVIDIA RTX 5070 installed in the Expansion Bay works under Linux using NVIDIA’s proprietary drivers. When swapping to the Dual M.2 Adapter, the NVIDIA driver is automatically disabled-eliminating driver conflicts or black screens. The Expansion Bay system is explicitly designed for dynamic hardware configurations.

Pricing and Realistic Configurations

Framework sells the Laptop 16 as a DIY kit (without RAM, SSD, or OS) and as a preconfigured system:

Source: frame.work, as of March 2026. Euro prices typically run 25-30% above the U.S. list price due to import VAT and shipping.

A realistic developer configuration-featuring a Ryzen AI 9 CPU, 32 GB DDR5 RAM, 1 TB NVMe SSD, and RTX 5070 GPU-comes in at around 1,743 euros. That’s on par with a Dell XPS 16 or Lenovo ThinkPad X1 Extreme, except Framework lets you swap out the GPU in two years instead of replacing the entire laptop.

Who the Framework Laptop 16 is worth it for

Conclusion

The Framework Laptop 16 isn’t a device for everyone. It’s aimed at developers and engineers who understand that a laptop’s total cost of ownership doesn’t end at the purchase price. If you need a GPU upgrade every two years, run Linux as your primary OS, and refuse to accept proprietary hardware, the Framework 16 delivers a tool that meets those demands.

No other manufacturer offers this combination of modular GPU, open firmware, and configurable ports. For IT departments that evaluate cloud stacks locally and therefore face shifting hardware requirements, this is a compelling advantage. For pure office use, however, lighter and more affordable alternatives exist.

Frequently Asked Questions

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