What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
For GGUF models in llama.cpp, GPU offloading means keeping as many model layers as practical in GPU memory; CPU or hybrid execution lets system RAM and the CPU handle layers that do not fit in VRAM. GPU-heavy placement is a sensible starting point when the model and runtime memory fit, while CPU or hybrid placement can make a larger model usable at the cost of potentially slower inference. The right choice depends on the model, context, hardware, backend, and workload—not a universal CPU-versus-GPU speed rule.
What CPU and GPU offloading mean for a GGUF model
In llama.cpp, the placement control is usually described from the GPU side. The -ngl, --n-gpu-layers, or --gpu-layers option sets the maximum number of model layers to keep in VRAM. The documented default is auto; all or a high layer count requests that as many layers as possible be placed on GPUs. It does not guarantee that every layer will fit. See the llama.cpp multi-GPU guide.
If GPU memory cannot hold the model’s weights and other runtime allocations, remaining work can run using system RAM and the CPU. This is commonly called CPU offloading or hybrid placement. It is a way to trade performance for capacity, not a performance upgrade by definition: the llama.cpp guide notes that system RAM is comparatively slower when weights cannot stay on one GPU.
CPU-heavy or hybrid placement versus GPU-heavy placement
| Consideration | CPU-heavy or hybrid | GPU-heavy |
|---|---|---|
| Capacity | Can use system RAM when the desired model does not fit in VRAM; system RAM must still be sufficient. | Keeps more layers in VRAM when capacity permits. |
| Speed | More CPU execution can be much slower, depending on CPU, memory bandwidth, backend, and workload. | Can improve performance with a suitable GPU backend and enough memory; measure rather than assume. |
| Memory pressure | Uses host memory and can increase system RAM pressure. | Needs VRAM for weights as well as runtime buffers and the KV cache. |
| Setup | Requires a supported CPU backend; CPU thread controls can be tuned. | Requires a build with the appropriate GPU backend and a suitable layer-placement setting. |
| Useful when | The model exceeds available VRAM, or no supported accelerator is available. | The intended model and workload fit in available GPU memory. |
This is a qualitative comparison, not a benchmark or a claim that every CPU is slower than every GPU configuration. There is no portable tokens-per-second figure for CPU versus GPU without the model, hardware, backend, settings, and measured workload.
Free tools Windows power users keep installed
One-click scans. No signup required.
#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
- Convert USB-A Ports to USB-C: Designed to connect USB-C earphones, cables, flash drives, card readers, and other USB-C accessories to standard USB-A ports. Plug-and-play with no drivers or software required.
- Aluminum Alloy Housing: Built with a sturdy aluminum alloy shell that aids in heat dissipation and protects against daily wear and scratches. Designed to maintain a stable and secure connection.
- Compact & Travel-Friendly: The ultra-compact design allows the adapter to stay plugged into your device without blocking adjacent ports or adding bulk, reducing wear and tear on your original USB ports.
- 12-Month Warranty: Backed by a 12-month manufacturer warranty for peace of mind. Designed to meet strict quality control standards for reliable everyday performance.
How to choose a placement for your workload
- Check the whole memory demand. Account for model weights, runtime buffers, and KV cache rather than comparing the model file size with VRAM alone. Context size affects memory: the llama.cpp guide says KV-cache size is roughly proportional to
n_ctx. - Start GPU-heavy if it fits. If the desired workload fits in one GPU’s VRAM, a GPU-heavy configuration is a reasonable starting point. Use
--n-gpu-layers autoor requestall, then verify the runtime log reports the expected backend and placement. - Use partial placement when it does not fit. Let supported CPU execution handle remaining layers, reduce context or choose a smaller or more-quantized model, or use multiple GPUs if supported by your setup.
- Measure the work you actually do. Benchmark prompt processing and token generation separately. Their performance can differ, so a single speed observation may not describe your intended use.
llama.cpp controls that affect placement and memory
-ngl,--n-gpu-layers, or--gpu-layerssets the maximum number of layers to keep in VRAM.autois the documented default;allor a high count requests all possible GPU layers.-tor--threads, and-tbor--threads-batch, control CPU threads. The best values depend on the machine and workload; the llama.cpp CLI reference documents these options.-cor--ctx-sizesets context size. Lowering it can reduce memory pressure because KV-cache demand scales roughly withn_ctx; confirm that a smaller context still suits the task.--fitcan automatically fit unset parameters to device memory, but the guide says it is not supported with tensor split. Context may need to be set manually.
Do not treat a layer count as universal: memory also goes to buffers and KV cache, and automatic fitting and split-mode behavior depend on configuration.
When multiple GPUs change the decision
Multiple GPUs can help when one GPU lacks enough VRAM, but the split strategy and interconnect affect performance. The llama.cpp maintainers’ guide summarizes the trade-off: “Pipeline-parallel maximizes batch throughput; tensor-parallel minimizes latency.”
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
--split-mode layer: The default pipeline-parallel mode. GPUs hold contiguous layers and their corresponding KV cache; the guide describes this as the most compatible choice.--split-mode tensor: Experimental tensor parallelism, intended to reduce token-generation latency. It splits weights and KV across participating GPUs, requires Flash Attention, currently disallows quantized KV cache, and is not implemented for every architecture. Its performance depends more on GPU interconnect.
Choose a mode for the relevant objective and supported architecture, not simply because more GPUs are present. The multi-GPU guide covers the modes and their current limitations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to do if GPU memory runs out
Out-of-memory remedies depend on the configuration. For tensor-mode OOM, the llama.cpp guide suggests lowering context first, then reducing server parallelism, then lowering GPU layers; layers that no longer fit move to CPU and inference may become much slower. Follow the guidance for the mode you are using rather than assuming every OOM has the same fix.
Quick Recap
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
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 at the time of purchase will apply.
