ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition Gaming Graphics Card
4.7Editor score
16GB GDDR7 memory for entry AI
767 AI TOPS performance rating
Compact 2.5-slot design for small cases
In this guide
Choosing the right GPU for Llm workloads requires careful consideration of memory capacity and architecture. Large language models demand significant VRAM to run efficiently without offloading. This guide helps you navigate the latest hardware options available in the market today.
We have evaluated ten distinct graphics cards ranging from consumer gaming models to professional workstation accelerators. Each product was selected based on its memory size, bus width, and compatibility with modern AI frameworks. Our analysis focuses on performance metrics relevant to local AI deployment scenarios.
Each pick highlights specific strengths such as cooling efficiency or multi-GPU scalability for complex clusters. Prices and availability can change frequently so verify current stock before purchasing. We recommend checking official documentation for driver support with your specific operating system environment.
Top 3 Picks for Best GPU for Llm
ASUS Turbo Radeon AI PRO R9700 32GB Graphics Card Built for AI workflows
3.6Editor score
32GB GDDR6 VRAM for large models
PCIe 5.0 for multi-GPU scaling
128 AI Accelerators for fast inference
NVD RTX PRO 6000 Blackwell Professional Workstation Edition Graphics Card for AI Design Simulation Engineering
4.3Editor score
96GB GDDR7 ECC memory capacity
PCIe Gen 5 high bandwidth support
5th Gen Tensor Cores for FP4 precision
Top 10 Best GPU for Llm in 2026 Compared
The following table provides a side-by-side comparison of all ten products evaluated in this guide. We focus on key specifications that directly impact Llm performance such as memory size and interface speed.
ProductsSpecificationsEditor scorePrice
Best Budget
GIGABYTE GeForce RTX 5080 Gaming OC 16G Graphics Card
16GB GDDR7 Memory
256-bit Memory Interface
NVIDIA Blackwell Architecture
PCIe 5.0 Support
4.6Editor score
Editor's Choice
ASRock Intel Arc Pro B60 Creator 24GB Graphics Card
24GB GDDR6 Memory
192-bit Memory Interface
Intel Xe2-HPG Architecture
PCIe 5.0 Support
3.9Editor score
Best Premium
ASRock Radeon AI PRO R9700 Creator 32GB Professional Graphics Card
32GB GDDR6 Memory
256-bit Memory Interface
AMD RDNA 4 Architecture
PCIe 5.0 Support
4.4Editor score
GIGABYTE Radeon RX 9070 XT Gaming OC 16G Graphics Card
16GB GDDR6 Memory
PCIe 5.0 Support
WINDFORCE Cooling System
Gaming Optimized Design
4.7Editor score
ASUS TUF Gaming GeForce RTX 5080 16GB GDDR7 OC Edition Graphics Card
16GB GDDR7 Memory
NVIDIA Blackwell Architecture
Military-grade Components
3.6-slot Design
4.6Editor score
ASUS Turbo Radeon AI PRO R9700 32GB Graphics Card Built for AI workflows
32GB GDDR6 Memory
128 AI Accelerators
PCIe 5.0 Interface
Multi-GPU Scaling Support
3.6Editor score
ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition Gaming Graphics Card
16GB GDDR7 Memory
767 AI TOPS
2.5-slot Design
Blackwell Architecture
4.7Editor score
Tesla L40S 48GB AI HPC Graphics Accelerator
48GB AI Accelerator Memory
HPC Optimized Design
Enterprise Grade Reliability
High Performance Compute
NVlDlA RTX PRO 6000 Blackwell max-Q Workstation Ed. 96GB GDDR7 ECC GPU
96GB GDDR7 ECC Memory
512-bit Bus Width
300 Watts Max Power
Max-Q Workstation Edition
NVD RTX PRO 6000 Blackwell Professional Workstation Edition Graphics Card for AI Design Simulation Engineering
96GB GDDR7 ECC Memory
PCIe Gen 5 Support
5th Gen Tensor Cores
600W Power Load Design
4.3Editor score
1. ASUS Turbo Radeon AI PRO R9700 – Best Overall GPU for Llm
ASUS Turbo Radeon AI PRO R9700 32GB Graphics Card Built for AI workflows
32GB GDDR6 VRAM
256-bit Memory Bus
128 AI Accelerators
PCIe 5.0 Interface
This card is explicitly built for running LLMs locally with 32GB of VRAM to handle large models without offloading. It scores 3.6 out of 5 based on user feedback regarding its professional workflow capabilities. The standout quality is its dedicated AI acceleration hardware designed for inference tasks.
Pros
- Large 32GB memory capacity
- Optimized for AI inference
- Multi-GPU scaling support
- Efficient phase-change thermal pad
- Robust diecast shroud design
Cons
- Lower rating score than others
- Professional pricing tier
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Key strengths include up to 1531 TOPS of INT4 performance which enables fast fine-tuning and inference cycles. The 256-bit bus provides up to 640GB/s bandwidth ensuring data moves quickly between memory and cores. Wave-pattern design cuts memory temperature by up to 16% keeping clocks steady during long runs.
Trade-offs involve the lower user rating compared to gaming cards which may reflect driver maturity rather than raw performance. Professional drivers require validation steps for some applications. These are acceptable for enterprise use where stability and memory capacity outweigh gaming benchmarks.
Buyers seeking a workstation solution for local AI clusters will find this model ideal. The PCIe 5.0 support allows dense multi-GPU builds for training and inference clusters. It is recommended for teams needing reliable local execution without cloud latency or subscription overhead.
Large Memory Capacity for Local Models
The 32GB GDDR6 VRAM allows you to load large language models directly without offloading to system RAM.
Efficient Thermal Design
Phase-change thermal pads and wave-pattern cooling reduce temperatures significantly during sustained heavy AI workloads.
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2. GIGABYTE GeForce RTX 5080 – High Performance AI Option
GIGABYTE GeForce RTX 5080 Gaming OC 16G Graphics Card
16GB GDDR7 Memory
256-bit Memory Interface
NVIDIA Blackwell Architecture
WINDFORCE Cooling
This model leverages the NVIDIA Blackwell architecture and includes 16GB of GDDR7 memory. It holds a 4.6 rating which indicates strong user satisfaction with its performance and reliability. The standout feature is its integration with DLSS 4 and PCIe 5.0 for modern workflows.
Pros
- Next-gen GDDR7 memory speed
- Blackwell architecture support
- High user rating
- Efficient cooling system
- PCIe 5.0 support
Cons
- 16GB VRAM limits model size
- Higher price point
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Strengths lie in its high-speed memory interface and cooling system designed for sustained loads. WINDFORCE cooling ensures temperatures stay manageable during compute tasks. The PCIe 5.0 interface provides maximum bandwidth for data transfer to the CPU.
Trade-offs include the 16GB limit which may constrain very large models to quantization or offloading. This is acceptable for mid-range deployments where speed matters more than absolute model size. The price reflects premium features that justify the cost for performance seekers.
Buyers prioritizing speed and modern architecture should consider this card for local AI tasks. It fits well in systems needing balanced performance for gaming and inference. Verify system power requirements as high-end cards often need robust PSUs.
Advanced Architecture Benefits
NVIDIA Blackwell architecture provides enhanced efficiency for AI tasks compared to previous generations.
Memory Speed Improvements
GDDR7 memory delivers faster data transfer rates improving overall throughput for inference workloads.
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3. ASRock Intel Arc Pro B60 Creator – Best for Creator Workflows
ASRock Intel Arc Pro B60 Creator 24GB Graphics Card
24GB GDDR6 Memory
192-bit Memory Interface
Xe2-HPG Architecture
PCIe 5.0 Support
This workstation GPU features 24GB of GDDR6 memory which is ideal for LLMs and generative design. It holds a 3.9 rating reflecting its position in the professional sector rather than consumer gaming. The standout feature is its optimized support for Linux multi-GPU LLM deployments.
Pros
- 24GB memory for large tasks
- 456 GB/s bandwidth
- Multi-GPU scaling support
- AV1/H.264 encode engines
- PCIe 5.0 interface
Cons
- New platform driver maturity
- Single fan blower design
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Strengths include a massive 456 GB/s bandwidth and Xe2-HPG architecture with 20 Xe cores and 160 XMX engines. It supports AV1 and H.264 encoding for video workflows alongside AI tasks. Multi-GPU scaling allows you to scale AI performance across multiple cards effectively.
Trade-offs involve driver maturity as new platforms may require updates for stable enterprise use. The blower-style cooling is efficient for multi-GPU builds but may be louder under full load. These are acceptable for server racks where density is prioritized over noise.
Buyers needing scalable GPU solutions for professional environments will find this card suitable. It supports up to four DisplayPort 2.1 outputs for expansive multi-monitor workflows. Verify chassis clearance as it uses a 2-slot card form factor with specific power requirements.
Dedicated Video Encode Support
Twin media transcoders support AV1 and H.264 for fast video export alongside AI processing.
Multi-GPU Deployment
Optimized for Linux multi-GPU LLM deployments allowing you to scale performance across multiple cards.
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4. ASRock Radeon AI PRO R9700 Creator – Best Large Memory Option
ASRock Radeon AI PRO R9700 Creator 32GB Professional Graphics Card
32GB GDDR6 Memory
256-bit Memory Interface
AMD RDNA 4 Architecture
Blower Cooling Design
This card offers 32GB of GDDR6 memory engineered for AI development and compute-intensive workloads. It holds a 4.4 rating indicating strong reliability and performance in professional environments. The standout quality is its massive memory capacity enabling large AI models to run fully on GPU.
Pros
- 32GB memory for massive models
- RDNA 4 with AI accelerators
- Vapor chamber heatsink
- Multi-GPU workstation design
- PCIe 5.0 support
Cons
- Professional workstation price
- Limited to specific ecosystems
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Strengths include RDNA 4 with 3rd Gen Ray Tracing and 2nd Gen AI Accelerators for groundbreaking performance. A vapor chamber heatsink ensures reliable cooling under sustained professional loads. The blower design exhausts heat directly out of the chassis ideal for dense server racks.
Trade-offs include the professional workstation price which reflects enterprise build quality. It may not be necessary for casual users who do not require 32GB VRAM. These are acceptable for labs needing consistent performance during long training cycles without thermal throttling.
Buyers with enterprise-grade needs should consider this model for its memory capacity and thermal reliability. The 2-slot design maximizes density in server racks and multi-GPU builds. Contact support directly for assistance with specific software compatibility checks.
Enterprise Thermal Solution
Vapor chamber heatsink with industrial thermal interface material ensures cooling under sustained professional loads.
Large Memory Capacity
32GB GDDR6 memory provides ample bandwidth for large AI models and complex rendering tasks.
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5. GIGABYTE Radeon RX 9070 XT – High Rating Gaming Option
GIGABYTE Radeon RX 9070 XT Gaming OC 16G Graphics Card
16GB GDDR6 Memory
PCIe 5.0 Support
WINDFORCE Cooling System
Gaming OC Design
This GPU delivers 16GB of GDDR6 memory and carries a high 4.7 user rating indicating strong satisfaction. It is powered by the Radeon RX 9070 XT architecture. The standout feature is its PCIe 5.0 support which enables faster data transfers for modern systems.
Pros
- High 4.7 user rating
- PCIe 5.0 support
- 16GB VRAM capacity
- Server-grade thermal gel
- RGB lighting options
Cons
- Gaming focus over AI tuning
- Limited professional features
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Strengths include a WINDFORCE cooling system with server-grade thermal conductive gel for stability. RGB lighting adds aesthetic appeal for gaming builds that also handle compute tasks. The gaming OC design ensures factory overclocks for better out of box performance.
Trade-offs involve less specific AI tuning compared to workstation cards. The gaming focus may lack enterprise features like certified drivers for professional apps. These are acceptable for hobbyists needing a versatile card that handles games and local inference well.
Buyers seeking a high rating card for dual use should consider this model. It fits well in gaming rigs that occasionally run AI models or rendering. Verify your system supports PCIe 5.0 to leverage the full bandwidth potential.
High User Satisfaction
The 4.7 rating reflects strong user satisfaction with performance and cooling efficiency.
Thermal Gel Integration
Server-grade thermal conductive gel helps maintain stable temperatures under heavy loads.
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6. ASUS TUF Gaming GeForce RTX 5080 – Durable High-End Card
ASUS TUF Gaming GeForce RTX 5080 16GB GDDR7 OC Edition Graphics Card
16GB GDDR7 Memory
NVIDIA Blackwell Architecture
Phase-change Thermal Pad
Military-grade Components
This card is powered by the NVIDIA Blackwell architecture and features military-grade components for durability. It holds a 4.6 rating reflecting its high quality and reliability. The standout feature is the phase-change GPU thermal pad ensuring optimal thermal performance under heavy loads.
Pros
- Robust military-grade build
- Blackwell architecture support
- Phase-change thermal pad
- High 4.6 rating
- Auto-Extreme manufacturing
Cons
- Requires 850W PSU minimum
- Large physical size
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Strengths include Auto-Extreme precision automated manufacturing and protective PCB coating against short circuits. A massive fin array optimized for airflow from three Axial-tech fans keeps temperatures low. GPU Tweak III software provides intuitive performance tweaking and system monitoring.
Trade-offs involve the requirement for a minimum 850W PSU with a specific 16-pin connector. Its 3.6-slot design requires significant case clearance. These are acceptable for high-end builds needing maximum reliability for compute tasks over long periods.
Buyers prioritizing durability and modern architecture should consider this model for serious workloads. It fits well in spacious cases with robust power supplies. Confirm case clearance and slot spacing before purchasing to avoid compatibility issues.
Military-Grade Durability
Military-grade components deliver rock-solid power and longer lifespan for ultimate durability.
Advanced Thermal Management
Phase-change GPU thermal pad outlasts traditional thermal paste ensuring optimal performance longevity.
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7. ASUS Dual GeForce RTX 5060 Ti – Best Budget AI Entry
ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC Edition Gaming Graphics Card
16GB GDDR7 Memory
767 AI TOPS
2.5-slot Design
Dual BIOS Support
This model delivers 16GB GDDR7 memory and 767 AI TOPS performance for entry AI workloads. It holds a high 4.7 rating reflecting strong value. The standout feature is its compact 2.5-slot design which maximizes compatibility in small chassis.
Pros
- Strong performance for price
- Compact 2.5-slot design
- 767 AI TOPS performance
- Dual BIOS profiles
- 0dB silent technology
Cons
- Lower memory than premium
- Limited multi-GPU support
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Strengths include Axial-tech fan design with a smaller fan hub for longer blades. A 2.5-slot design ensures compatibility in smaller cases. Dual BIOS lets you toggle between Quiet and Performance BIOS profiles for different use cases.
Trade-offs involve lower total memory compared to premium 32GB models but acceptable for many tasks. It lacks enterprise multi-GPU scaling options. These are acceptable for users needing a single card with strong performance and silence.
Buyers seeking a balance of cost and performance should consider this card. It is ideal for small form factor builds needing AI capabilities. Verify chassis clearance and power supply compatibility before purchasing.
Silent Operation
0dB technology lets you enjoy light gaming or idle in relative silence without fan noise.
Compact Fit
2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis.
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8. Tesla L40S 48GB – Dedicated HPC Accelerator
Tesla L40S 48GB AI HPC Graphics Accelerator
48GB AI Memory
HPC Optimized
Enterprise Reliability
Data Center Grade
This accelerator features 48GB of memory targeting high-performance computing tasks. It lacks a public rating because it is primarily sold through enterprise channels. The standout feature is its dedicated HPC architecture designed for rigorous data center environments.
Pros
- 48GB massive memory
- Dedicated HPC design
- Data center reliability
- High throughput support
- Professional validation
Cons
- No public user rating
- OEM or bulk packaging
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Strengths include high throughput capabilities for data science and AI training without consumer overhead. The design prioritizes reliability and efficiency over consumer aesthetics. It fits into server racks designed for professional clusters rather than desktop PCs.
Trade-offs include limited consumer compatibility and lack of gaming features. You may need specific drivers and server mounting hardware. These are acceptable for labs requiring maximum reliability and memory capacity for large scale training.
Buyers requiring high memory capacity and server-grade stability should consider this model. It is ideal for environments where uptime and throughput are critical. Check vendor requirements for power and thermal specifications.
Enterprise Memory Capacity
48GB of AI memory allows handling extremely large datasets without constant offloading to system RAM.
HPC Focused Design
Optimized specifically for high-performance computing tasks ensuring stability under constant heavy loads.
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9. NVIDIA RTX PRO 6000 max-Q – Max-Q Professional Powerhouse
NVlDlA RTX PRO 6000 Blackwell max-Q Workstation Ed. 96GB GDDR7 ECC GPU
96GB GDDR7 ECC Memory
512-bit Bus Width
300W Max Power
Max-Q Workstation Edition
This workstation monster packs 96GB of next-gen GDDR7 ECC memory for agentic workflows. It lacks a public rating due to its professional sales channels. The standout quality is eliminating VRAM bottlenecks with a 512-bit bus width for local open-source LLMs.
Pros
- 96GB massive memory
- Enterprise ECC support
- Low 300W max power
- Multi-GPU desktop scaling
- PCIe 5.0 support
Cons
- OEM bulk packaging
- Requires specific OS validation
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Strengths include delivering up to 3511 TOPS while capping power consumption at 300 Watts. This allows multi-GPU desktop scaling which is rare for high power GPUs. It supports PCIe 5.0 and DisplayPort 2.1 outputs for high resolution displays.
Trade-offs involve OEM bulk packaging without retail accessories or full warranty support in all regions. You must validate software compatibility for specific enterprise apps. These are acceptable for researchers needing massive memory for local pipelines.
Buyers needing enterprise reliability and massive memory capacity should consider this model. It bridges the gap between intense calculation and cinematic visual output. Contact support for export license requirements if purchasing outside the US.
Memory Capacity Benefits
96GB of memory lets you host heavy open-source LLMs locally without cloud latency.
Efficient Power Design
300 Watts cap allows multi-GPU desktop scaling which is unusual for GPUs of this power.
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10. NVIDIA RTX PRO 6000 – Best Professional GPU
NVD RTX PRO 6000 Blackwell Professional Workstation Edition Graphics Card for AI Design Simulation Engineering
96GB GDDR7 ECC Memory
PCIe Gen 5 Support
5th Gen Tensor Cores
Universal MIG Support
This card offers 96GB of GDDR7 ECC memory designed for enterprise AI workloads. It holds a 4.3 rating reflecting high reliability. The standout feature is Universal MIG support allowing a single card to be divided into isolated instances.
Pros
- 96GB ECC memory
- 5th Gen Tensor Cores
- Universal MIG support
- PCIe Gen 5 bandwidth
- 4th Gen Ray Tracing
Cons
- Export license requirements
- Requires enterprise setup
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Strengths include 5th Gen Tensor Cores delivering up to 3X performance of previous generations with FP4 precision. PCIe Gen 5 doubles bandwidth improving data transfer speeds from CPU memory. 4th Gen Ray Tracing doubles intersection rates for realistic visual effects.
Trade-offs involve strict export requirements for buyers outside the US requiring an export license. OEM packaging lacks retail accessories. These are acceptable for teams needing secure isolation for different users or workloads on one card.
Buyers needing secure isolation and massive memory should consider this model for enterprise work. It fits seamlessly into standard desktops enabling rapid scaling of intelligence. Check current prices and warranty terms before purchasing as terms vary by region.
Multi-Instance GPU Support
Universal MIG divides the GPU into multiple instances each with dedicated resources for concurrent workloads.
Enterprise Memory Speed
PCIe Gen 5 support provides double the bandwidth of PCIe Gen 4 for faster data transfer.
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Buying Guide – How to Choose the Best GPU for Llm
Selecting the right hardware requires balancing memory needs with architecture support. Here are the key factors to consider when building a local AI system.
VRAM Capacity
Large language models consume significant memory to store weights during inference. Models like Llama 3 or similar open-source variants often require at least 16GB to run efficiently without quantization. Professional cards with 32GB or more allow full precision models to run locally.
Look for cards with at least 16GB VRAM to ensure you can run mid-sized models. For large-scale deployments consider 32GB or higher to avoid system bottlenecks.
Memory Bus Width
The memory bus width determines how fast data travels between the GPU and VRAM. Wider buses allow higher bandwidth which speeds up inference times for large contexts. Cards with 256-bit or wider buses perform better with high model sizes.
Prioritize GPUs with 256-bit or wider interfaces to ensure smooth data flow during computation.
Architecture Generation
Newer architectures provide better efficiency and dedicated AI hardware. NVIDIA Blackwell or AMD RDNA 4 include specific cores for AI tasks improving performance per watt. Older cards may lack optimization for modern AI frameworks.
Check that the GPU supports the latest architecture standards to benefit from AI-specific optimizations.
PCIe Interface
PCIe version affects data transfer rates between the CPU and GPU. PCIe 5.0 offers double the bandwidth of PCIe 4.0 which helps when training or loading massive models quickly. Ensure your motherboard supports the interface to avoid bottlenecks.
Verify your motherboard has PCIe 5.0 slots if you plan to use high-speed transfer cards.
Power Supply Needs
High-end GPUs require significant power to operate at peak efficiency. A robust power supply ensures stable performance during long training sessions or inference tasks. Verify that your unit meets the wattage and connector requirements of your chosen GPU.
Confirm your power supply meets the minimum wattage and specific connector requirements for the card.
Cooling Solutions
Sustained AI workloads generate heat that can cause thermal throttling. Blower-style coolers are ideal for multi-GPU setups where space is tight. Standard fan designs work well in open cases but may be noisier under load.
Choose a cooling solution that fits your case and workload intensity to prevent overheating.
Software Ecosystem
Driver support and framework compatibility are vital for smooth operation. Professional cards offer validated drivers for enterprise applications whereas gaming cards may need manual tuning. Check if the GPU supports your preferred AI frameworks like PyTorch or TensorFlow.
Ensure the GPU is compatible with your AI frameworks and has stable drivers for your OS.
Multi-GPU Scaling
Some applications benefit from combining multiple GPUs to increase VRAM and compute power. Linux based environments often support multi-GPU scaling for training clusters. Check if the card supports NVLink or similar interconnects for high bandwidth data transfer between cards.
Verify multi-GPU compatibility if you plan to scale performance across multiple cards.
How to Use and Care for Your GPU for Llm
Install the GPU securely into a compatible PCIe slot ensuring it is fully seated. Verify that power connectors are firmly plugged in to prevent instability during high load tasks.
Download and install the latest drivers from the manufacturer to ensure compatibility with AI frameworks. Update your BIOS if necessary to support PCIe 5.0 speeds and power management features.
Monitor temperatures using software like GPU Tweak III or similar tools to detect overheating. Clean dust regularly to maintain airflow and cooling efficiency for sustained operation over time.
Frequently Asked Questions
How much VRAM do I need for running local LLMs?
For most open-source models 16GB is a good baseline to run 7B to 13B parameter models efficiently. If you want to run larger models like 70B parameter variants you should look for 32GB or more to avoid offloading to system RAM which slows down inference.
Does PCIe 5.0 matter for AI inference?
PCIe 5.0 provides double the bandwidth of PCIe 4.0 which helps when loading large models or transferring data between CPU and GPU. While it is not strictly necessary for basic inference it significantly speeds up workflows involving large context windows.
Can I use a gaming GPU for AI tasks?
Yes most gaming GPUs with sufficient VRAM like the RTX 5060 Ti or higher can run local AI inference efficiently. They may lack specific enterprise features or certifications but they perform well for hobbyists and small scale deployments.
What is ECC memory and do I need it?
ECC stands for Error Correction Code which helps prevent data corruption during long training sessions. While not strictly required for inference it is valuable for professional environments where data integrity is critical for model accuracy.
Do these GPUs support Linux?
Most professional and high-end gaming GPUs support Linux with the correct drivers installed. Check manufacturer documentation for specific distro support especially for multi-GPU setups which may require additional configuration steps.
How do I verify if my case fits these GPUs?
Check the dimensions listed in the product specifications for length and slot width. Professional cards often use 2-slot designs while high-end gaming cards may require 3.6 slots or more. Measure your case clearance before purchasing.
Final Thoughts on Choosing the Best GPU for Llm
This guide highlighted the top 10 GPUs for Llm workloads ranging from budget gaming cards to premium professional accelerators. Cards like the ASUS Turbo Radeon AI PRO R9700 offer massive memory for local clusters while the RTX 5080 series brings high speed for general inference.
When choosing prioritize VRAM capacity and memory bandwidth over raw gaming benchmarks. Look for PCIe 5.0 support and efficient cooling to ensure stable performance during long training sessions or inference tasks.
Prices and availability change frequently so verify current stock and warranty terms before purchasing. Check official documentation for driver support with your specific operating system environment.