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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 coverage. Choose by available VRAM first: the RTX 5090 has 32GB, while the RTX 4090 and RTX 3090 each have 24GB. AMD’s Radeon RX 7900 XTX also has 24GB and can be a good fit when the exact application, operating system and backend are confirmed to support it.
The right choice depends on whether the model, its context and the runtime overhead fit in memory—not just on raw speed. This guide compares the leading cards in the available figures and explains how to check software compatibility before you buy.
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Which GPU is best for running AI models locally?
For the broadest chance of getting a local AI application working on either Windows or Linux, start with an NVIDIA GeForce RTX GPU. CUDA compatibility and NVIDIA’s TensorRT-family tools give NVIDIA the least-surprising software path across applications. If you need the largest memory pool among the cards compared here, the RTX 5090’s 32GB makes it the high-end consumer choice; if you want 24GB, compare the RTX 4090, RTX 3090 and RX 7900 XTX by backend support, price and condition.
There is no universal best card independent of workload. A GPU that is fast in one model-and-runtime combination may be slower in another, and a card that lacks enough memory for a chosen workload cannot make up for that limitation with extra compute. Treat any token-rate comparison as specific to its test conditions, not as a promise of what your own setup will deliver.
#1 Best Overall
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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.
Quick recommendations
- Maximum consumer-card memory and throughput: RTX 5090, if its cost, power and system requirements suit your build.
- Fast NVIDIA option for a workload that fits in 24GB: RTX 4090.
- Potential value from a used card: RTX 3090, with careful checks of condition, warranty, cooling and power needs.
- AMD alternative: RX 7900 XTX when the exact application and OS have confirmed ROCm or Vulkan support.
How much VRAM do I need for a local LLM?
There is no single VRAM number that guarantees every model will run. The model weights, KV cache for the chosen context, and runtime overhead all consume memory; the combined workload has to fit in the GPU’s available pool. Capacity is therefore the first filter, before comparing bandwidth or benchmark speed.
Model size alone does not determine fit. Quantization and context length affect the memory requirement, as do choices made by the application and runtime. A model that loads with a short context may not leave enough room for a longer one. Likewise, two applications using the same model can have different overhead or GPU-offload behavior. Check the target runtime’s current requirements for the exact model, quantization and context you intend to use rather than relying on a generic “model size” rule.
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- Name the actual workload: identify the model, quantization, context length and runtime rather than shopping for an abstract model category.
- Check the runtime’s memory guidance: look for its requirements or supported settings for that combination, including whether the model is expected to fit entirely on the GPU.
- Compare that requirement with available VRAM: leave room for the KV cache and runtime overhead. Do not assume every gigabyte on a card is available for model weights.
- Confirm behavior on your OS and backend: successful detection of a GPU does not by itself prove that the chosen application can run the workload on it.
- Test the context you expect to use: a setup that works at one context length may run out of memory when the context grows.
Can I run a 32B or 70B model on one graphics card?
Model parameter count by itself is not enough to answer yes or no. Whether a 32B or 70B model can run on one card depends on the model’s quantization, the context, runtime overhead and how the software uses GPU memory. The cards in this comparison offer 24GB or 32GB, but those capacity figures do not establish that a particular 32B or 70B workload will fit.
Before buying for a named model, check the exact model-and-runtime configuration against the application’s current guidance, including any supported partial GPU offload if full GPU residency is not required. If the required memory exceeds what the card can provide, a faster GPU does not solve the capacity problem. A larger-memory card may make more configurations possible, but a fit still needs to be verified for the intended context and software.
How do the leading GPUs compare?
The table separates published card specifications from a sample of database token-generation results. Specifications are listed in a 2026 comparison by CompareAIHardware. Token rates are sample rows reported by LocalLLMBench under that site’s test conditions; they are not directly comparable guarantees for every model or build.
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
| GPU | VRAM | Memory bandwidth | Board power | Sample token generation | Practical reading |
|---|---|---|---|---|---|
| GeForce RTX 5090 | 32GB | 1,792GB/s | 575W | 264 tok/s | Largest memory pool and strongest single-consumer-card option in this comparison; check system power and cooling. |
| GeForce RTX 4090 | 24GB | 1,008GB/s | not stated (CompareAIHardware, 2026) | 188 tok/s | Fast NVIDIA choice when the workload fits within 24GB. |
| GeForce RTX 3090 | 24GB | 936GB/s | not stated (CompareAIHardware, 2026) | 160 tok/s | Still relevant for its memory capacity, particularly when a used-card price makes sense. |
| Radeon RX 7900 XTX | 24GB | 960GB/s | not stated (CompareAIHardware, 2026) | 191 tok/s | Credible AMD option if your exact software stack supports the card on your OS. |
CompareAIHardware’s 2026 figures list VRAM, bandwidth and, for the RTX 5090, board power. LocalLLMBench’s sample rows place the 5090 at 264 tok/s, RX 7900 XTX at 191 tok/s, RTX 4090 at 188 tok/s and RTX 3090 at 160 tok/s. These values should not be read as a controlled head-to-head result unless the model, quantization, context, batch, backend, driver, host and build are aligned. Token rates can change with each of those factors.
Is NVIDIA or AMD better for local AI on Linux?
NVIDIA is the safer default when compatibility breadth matters: CUDA-based tooling is widely supported, and NVIDIA also offers TensorRT-family tooling. AMD can work well, but the ROCm or Vulkan route needs to be checked for the particular application, Linux distribution, software version and card.
ROCm documentation includes the RX 7900 XTX as hardware, but a hardware listing is not the same as a guarantee that every local AI application supports it. Check the application’s own compatibility information for the backend it uses. Confirm that the documented combination covers your operating system and version, not merely the GPU model. If you plan to switch between Windows and Linux, verify support separately for each OS rather than assuming a working configuration on one will transfer to the other.
Compatibility checks before choosing AMD
- Does the target application support ROCm, Vulkan, or another backend on the OS you will use?
- Does that support explicitly include the RX 7900 XTX and the relevant software version?
- Are the model-loading and GPU-use features you need supported, rather than only basic device detection?
- Is the documentation current for your planned OS and runtime version?
If any answer is unclear, treat compatibility as unconfirmed and look for a documented configuration or a way to test the exact stack before committing to a build. If you want the least friction across a range of local applications, NVIDIA is the more conservative choice.
Which GPU is the best value for Ollama or llama.cpp?
There is no reliable value winner without a current price and a benchmark for the exact model, quantization, context and backend you plan to use. For a workload that benefits from 24GB but does not require the latest card, an RTX 3090 can be worth considering on the used market. The RTX 4090 offers a faster NVIDIA option in the listed sample figures, while the RX 7900 XTX is an alternative if its software path is confirmed. The RTX 5090’s 32GB may be worthwhile when a specific workload needs the extra capacity or when its measured performance justifies the price.
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.
For Ollama or llama.cpp, verify current support in the version you intend to install instead of relying on a generic claim about the GPU brand. A runtime’s backend support, GPU-offload behavior and available settings can change. Compare a benchmark only if its model, quantization, context, backend and configuration match your intended use closely; otherwise, use it as an illustration, not a purchase forecast.
A practical value comparison
- Set a workload requirement: choose the models and context lengths you actually expect to use, then establish the memory needed in your preferred runtime.
- Eliminate cards that do not meet it: prioritize sufficient VRAM over a higher token rate for a workload that cannot fit.
- Confirm the software path: check the runtime’s current GPU and OS support for each candidate.
- Compare complete system cost: include the card price, a suitable power supply and adequate case cooling, rather than comparing GPU prices alone.
- Check measured performance carefully: favor results using the same runtime and a similar model configuration; recognize that differences in test setup limit comparisons.
- For used cards, assess risk: inspect the seller’s condition information, warranty status, cooling and power requirements before deciding whether the discount is worthwhile.
What should I check for power, cooling and a used GPU?
Power and cooling are part of the GPU decision, not afterthoughts. The RTX 5090 is listed at 575W board power in the 2026 specification comparison, so a build around it needs particular attention to the system’s power supply and cooling. The available figures do not establish the board power of the other cards in this comparison; check the exact board-partner model and manufacturer guidance before finalizing a system.
Board-partner designs can differ, so confirm the dimensions, power connectors and cooling design of the specific model you are considering. For a used RTX 3090, inspect the card’s condition and ask what warranty, if any, remains. Evaluate the actual listing and seller rather than assuming all cards of a model have the same history or condition.
- Confirm the exact card model and its power requirements against the rest of the system.
- Make sure the case can accommodate the card and provide appropriate airflow.
- For a used purchase, establish condition and warranty terms before paying.
- Account for the possibility that a cheap used card needs more attention to cooling or carries less purchase protection.
How should I choose between the four cards?
Start with the model configuration you need to run, then choose the least costly card that has enough memory and a supported software path while meeting your performance and system requirements. The following decision guide keeps those trade-offs in order.
Choose the RTX 5090 when memory headroom matters most
Its 32GB VRAM is the largest capacity among the four compared cards, and its listed 1,792GB/s bandwidth is also the highest. It is the clearest pick here for larger quantized workloads that fit within 32GB, but the listed 575W board power makes power and cooling checks essential. Do not buy it on the assumption that every 32B or 70B model will fit; verify the specific runtime configuration.
Rank #4
- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- 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.
Choose the RTX 4090 when 24GB is enough and speed matters
The RTX 4090 combines 24GB with 1,008GB/s listed bandwidth and the broad NVIDIA software route. It makes sense when the chosen workload fits in its memory pool and the higher-end 32GB option is unnecessary or unsuitable. Its sample token rate is a database result, not a guarantee for your own application.
Choose the RTX 3090 when used-market value is compelling
The RTX 3090 still offers 24GB, which keeps it relevant for local AI workloads that fit in that capacity. It is older and slower than current flagships in the cited comparison, so the price and condition matter. A strong used-market value depends on the specific card’s warranty, cooling, power requirements and seller condition.
Choose the RX 7900 XTX when its backend support is verified
The RX 7900 XTX has 24GB and listed bandwidth of 960GB/s. Its sample token-generation figure is close to the RTX 4090’s in the cited database, but that does not prove equal performance in another model or runtime. Confirm application, OS and version support for ROCm or Vulkan before selecting it as an NVIDIA alternative.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Why can real-world token rates differ from published results?
Token generation is affected by more than the GPU model. Model quantization, context length, batch, backend, driver, host system and software build can all change the result. A database row is useful as a clue about performance under its own test conditions; it is not a universal ranking of cards.
When comparing two results, check whether both use the same model and quantization, equivalent context and batch settings, the same runtime and backend, and comparable drivers and host systems. If those details are not aligned or stated, avoid treating a small difference in tok/s as decisive. Prefer results that mirror the workload you plan to run, and treat the result as evidence for that setup rather than every possible 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.
Frequently Asked Questions
Can I use the same local AI GPU setup on Windows and Linux?
Not necessarily. Applications can use different backends or have different support on each operating system, so confirm the target runtime’s documented configuration separately for Windows and Linux.
Does more memory bandwidth always mean faster local inference?
No. Bandwidth is one specification, while inference speed also depends on the model, quantization, context, batch, backend and software build. Use a benchmark matching your intended workload rather than ranking cards by bandwidth alone.
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It can be a sensible option when the 24GB capacity fits your workload and the used price compensates for the age and purchase risk. The exact card’s condition, remaining warranty, cooling and power needs matter as much as the model name.
Are token-per-second figures comparable between benchmark websites?
Only with caution: different sites may test different models, configurations, backends and host systems. A result is most useful when the test conditions are disclosed and closely resemble the application setup you intend to use.
Quick Recap
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.
