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For most people running AI models locally on Windows or Linux, an NVIDIA GeForce RTX card is the safest starting point because CUDA-based tools have broad compatibility. Choose the RTX 5090 when you need the largest memory pool and strongest single-card option in this comparison; consider the RTX 3090 for a potentially attractive used 24GB card. AMD’s RX 7900 XTX can be a good fit, but only after confirming that your exact application supports its ROCm or Vulkan backend on your operating system. First check VRAM: a faster card cannot run a workload that does not fit in its memory.
What matters most when choosing a GPU for local AI?
Start with VRAM capacity, then confirm software support, and only then compare speed and purchase cost. A local model needs room not just for its weights, but also for the KV cache used during inference and for runtime overhead. If the total requirement exceeds available memory, more compute throughput will not prevent an out-of-memory failure.
There is no universal “best GPU” for every local AI workload. The right card depends on the model, its quantization, context length, runtime, operating system, and whether the workload is inference or something else. As NVIDIA Developer puts it in Build Local AI With NVIDIA GPUs: “For additional details, choose hardware based on operating system, available GPU or unified memory, model size, and workflow.”
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Use this order of checks
- Model fit: Find the model and quantization you intend to run, and check its memory requirements in the runtime you plan to use. Include the context length and any other memory the runtime reserves.
- Backend support: Verify that the application supports the GPU backend on your specific OS and version. NVIDIA’s CUDA path is the broader default in this comparison; AMD support depends more on the particular ROCm or Vulkan setup.
- Performance: Compare results only for the same model, quantization, context, runtime, and backend. A token-per-second figure from a different setup may not predict your result.
- System fit: Check the card’s power requirements, your power supply, and case cooling. The RTX 5090’s listed board power is 575W, so plan around the specific board and complete system rather than treating the GPU alone as the power budget.
- Purchase risk: Compare current prices, availability, warranty, and condition. These figures change, and a used card’s condition can matter as much as its headline VRAM.
Which GPUs are the strongest options for local AI?
The RTX 5090 leads this set on memory capacity and listed bandwidth. The RTX 4090, RTX 3090, and RX 7900 XTX each have 24GB, so the deciding factors among them are software support, measured performance for your workload, system requirements, and price.
#1 Best Overall
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
| GPU | VRAM | Memory bandwidth | Listed board power | Practical fit |
|---|---|---|---|---|
| GeForce RTX 5090 | 32GB GDDR7 | 1,792GB/s | 575W | High-end single-card option when the model and runtime fit in 32GB |
| GeForce RTX 4090 | 24GB | 1,008GB/s | Not stated | Fast NVIDIA option for workloads that fit within 24GB |
| GeForce RTX 3090 | 24GB | 936GB/s | Not stated | Potential used-market value option with a substantial memory pool |
| Radeon RX 7900 XTX | 24GB | 960GB/s | Not stated | Alternative when the target application and OS support the chosen ROCm or Vulkan path |
VRAM and bandwidth figures in this table are from CompareAIHardware’s 2026 comparison. Board power is listed there for the RTX 5090 only; the other values are not stated in that comparison. The table is a specification comparison, not a universal ranking of model-generation speed.
GeForce RTX 5090: maximum single-card headroom
The RTX 5090 is the high-end consumer choice here for workloads that benefit from its 32GB of GDDR7 and 1,792GB/s listed bandwidth. That extra memory over a 24GB card can make a difference when a model, longer context, or runtime overhead pushes a workload beyond the smaller card’s usable capacity. It does not guarantee that every 32B or 70B model will run: the memory required varies with model format, quantization, context, and runtime.
CompareAIHardware lists the card at 575W board power. Account for the specific card’s requirements, power supply capacity, and case cooling before buying; do not size the rest of the system from the GPU’s memory specification alone.
GeForce RTX 4090: fast CUDA option with 24GB
The RTX 4090 has 24GB of VRAM and 1,008GB/s listed bandwidth. It remains a strong choice for models and contexts that fit within its memory, with the benefit of NVIDIA’s broad CUDA-based software path. If a target workload needs more memory than the card can provide, its speed does not solve that limit.
Rank #2
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
GeForce RTX 3090: older card worth considering used
The RTX 3090 also provides 24GB, with 936GB/s listed bandwidth. Its appeal is the combination of that memory capacity and the possibility of finding one on the used market; it is older and slower than current flagships. Treat a used listing as a system purchase, not just a VRAM deal: inspect the seller’s condition information, warranty status, power requirements, and cooling condition.
Radeon RX 7900 XTX: capable when the software path is confirmed
The RX 7900 XTX offers 24GB and 960GB/s listed bandwidth. ROCm documentation includes the hardware, but that alone does not establish support in every local-AI application, OS, or software version. Before choosing it, check the application’s current documentation for the exact GPU, backend, and operating system you intend to use. Do not assume that a feature working through one backend will work through another.
Is NVIDIA or AMD better for local AI on Windows and Linux?
NVIDIA is the lower-risk default across both operating systems because CUDA support is broader in the software paths covered here. AMD is a viable alternative for a verified workload, but compatibility needs more checking before purchase.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Consideration | NVIDIA GeForce RTX | AMD Radeon RX 7900 XTX |
|---|---|---|
| Common AI software path | Broad CUDA compatibility; NVIDIA also has TensorRT-family tooling | ROCm or Vulkan, depending on the application |
| Windows and Linux confidence | Safer general-purpose starting point across the two OS families | Confirm application, backend, OS, and version support individually |
| Memory in this comparison | 24GB on RTX 4090 and RTX 3090; 32GB on RTX 5090 | 24GB |
| Best decision rule | Choose when you want the broadest software compatibility | Choose when your exact application and setup are documented to work |
This is a general compatibility judgment, not a claim that every NVIDIA program works on every system or that AMD cannot run local models. Drivers, backends, and application support can change. Confirm compatibility against the versions you will actually install, particularly if using a less common runtime or a feature that depends on a specific backend.
Rank #3
- Powered by Radeon AI PRO R9700 - Supercharge you workflow with the cutting-edge RDNA 4 Architecture and 2nd-gen AI Accelerators.
- 32GB GDDR6 with 256-bit memory bus - Tackle larger, more complex projects without limits.
- PCIe Gen 5 - Unlock lightning-fast data transfers with PCIe Gen 5 support.
- GIGABYTE TURBO Fan Cooling System - Indented metal cover and blower fan increase airflow intake, while the vapor chamber, all copper heat sink, and metal frame offer efficient heat dissipation. Optimized airflow design allows for easy multi-GPU scalability.
- Double Ball Bearing Fan - Delivers superior heat resistance and rotational efficiency for better performance and a longer lifespan compared to conventional sleeve fans.
How much VRAM do you need for a local LLM?
There is no reliable one-number answer based only on a model’s parameter count. VRAM needs include model weights plus KV cache and runtime overhead, and the cache requirement changes with context and workload. A model that loads at one quantization or context length may not fit at another.
Check the full workload, not just the model name
- Model format and quantization: These affect the space taken by weights. Compare the exact file or format you plan to load.
- Context length: A longer context can increase memory used by the KV cache.
- Runtime overhead: The application and backend may need memory beyond the weights and cache.
- Available memory: A card’s marketed VRAM is not a promise that every byte will be available to the model at runtime.
Use the target runtime’s documented requirements, then leave room for cache and overhead rather than treating the model’s weight size as the complete memory budget. If the workload exceeds the GPU’s available memory, investigate whether that specific runtime supports a workable alternative; do not assume that adding a faster GPU or changing operating systems will make an oversized model fit.
Can one graphics card run a 32B or 70B model?
It depends on the model format, quantization, context length, runtime overhead, and the GPU’s available memory. The 32GB RTX 5090 gives more room than the 24GB cards in this comparison, but the supplied specifications do not establish a universal yes-or-no answer for either model size.
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For a particular model, check its actual memory requirements under the exact runtime and settings you expect to use. A 70B model may require trade-offs in representation or context, or a different memory arrangement, but whether those are supported depends on the software. Do not infer that a model will fit just because its parameter count appears close to a card’s VRAM number.
Rank #4
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- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
Which GPU gives the best value for Ollama or llama.cpp?
There is no defensible universal value winner without current prices and a benchmark for the exact model and runtime. For a budget-conscious build, the RTX 3090 is worth investigating because its 24GB memory pool remains useful and it is commonly considered as a used-market option. Compare its actual price and condition against a newer card, then test the workload you care about if possible.
For either Ollama or llama.cpp, verify the application’s current GPU backend support for your OS and card before buying. Then compare results using the same model, quantization, context, and runtime version. A card with higher raw bandwidth or a higher score in another application may not deliver the best performance per dollar for your chosen setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you interpret local-AI GPU benchmarks?
Benchmarks are workload-specific. Model quantization, context, batch, backend, driver, host system, and software build can all affect token-generation results, so figures from different sources are approximate comparisons rather than guarantees.
LocalLLMBench reports sample token-generation results of 264 tok/s for the RTX 5090, 191 tok/s for the RX 7900 XTX, 188 tok/s for the RTX 4090, and 160 tok/s for the RTX 3090. Those are results in that site’s database under its test conditions, not universal speeds for every model or application. Use them as an indication of one test set, not as a promise about Ollama, llama.cpp, or your own configuration.
Best Value
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
When comparing published results, ask whether the same model and quantization were used, and check the context, backend, and software versions. The most useful figure is one measured on the workload you expect to run—or one whose conditions closely match it.
What should you check before buying?
Use this checklist before committing to a card. It catches the common mistake of buying for a benchmark or VRAM number without checking whether the complete system and software can run the intended workload.
- Model and settings: Identify the model, quantization, and context length you plan to use.
- Memory fit: Confirm room for weights, KV cache, and runtime overhead in the target application.
- Operating system and backend: Verify the exact GPU, OS, application, and backend combination rather than relying on general brand support.
- Power and cooling: Check the specific board’s power requirements, your power supply, and the cooling capacity of the case. The RTX 5090 is listed at 575W board power.
- Price and condition: Compare current local pricing and warranty. For a used RTX 3090, inspect condition and seller terms rather than assuming every 24GB card is equally good value.
- Comparable performance data: Prefer results for the same runtime and model setup; label cross-source token rates as approximate.
Which card should you choose?
Choose the RTX 5090 if your priority is the largest VRAM pool and strongest single-card option in this comparison, and your system can accommodate its power and cooling needs. Choose the RTX 4090 if your workload fits in 24GB and you want a high-performance CUDA card. Consider a used RTX 3090 when its condition, warranty, and actual price make sense for your budget. Choose the RX 7900 XTX when you have confirmed support for your exact ROCm or Vulkan workload on your chosen OS. In every case, make model fit and backend support the first two checks—not the benchmark headline.
Frequently Asked Questions
Does a local-AI benchmark tell me how fast my own model will run?
Not necessarily. A published token rate is tied to its test setup, so a result from a different model, quantization, context, or backend may not transfer to your system.
Can I use these recommendations for training models, too?
The performance figures cited here are token-generation results, so they speak to inference rather than establishing training performance. For training, check the requirements and benchmarks for the specific training workflow instead of treating inference rankings as a training comparison.
Will a GPU with more memory always be faster?
No. More VRAM can make a larger workload fit, while speed depends on the GPU and the software and model setup. Capacity and throughput answer different questions.
Quick Recap
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