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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Choose the GPU that fits your models and software, not the one with the highest advertised compute number. Start by listing the exact workloads, model sizes, context lengths, concurrency and operating system you plan to use. Then verify that the model, runtime and working data fit in GPU VRAM (or unified memory), confirm that your application supports the exact card, and compare performance under a workload similar to yours. Finally, price the complete system, including power, cooling and electricity.
A faster GPU that cannot load your model, lacks a required software backend or forces an expensive system upgrade is a worse purchase than a slower card with enough memory and reliable support.
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1. Define the workload before looking at graphics cards
“Local AI” covers very different jobs. A card that is sensible for occasional chat with a small quantized language model may be inadequate for fine-tuning, image generation or serving several users. Write down the applications and models you actually expect to run, then classify each job.
Interactive inference
For a local chatbot, coding assistant or document question-answering tool, the key measures are whether the model fits, time to first token and sustained generation speed at your target context length. Occasional single-user use usually needs less throughput than an API serving many simultaneous requests.
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Fine-tuning and training
Fine-tuning generally needs more memory than inference because gradients, optimizer states and activation data may be held in addition to model weights. Training also creates sustained heat and power demand. Check the framework’s supported precision, kernels and distributed-training options before assuming a card can train a model simply because it can run inference.
Image and video generation
Diffusion pipelines vary with the model, resolution, control modules, batch size and whether an image or video workflow is used. Compare results for the exact pipeline and resolution you intend to run. A language-model benchmark does not predict image-generation performance.
Multi-user or automated serving
For an internal API, agent system or batch queue, concurrency and tail latency matter more than a single-user speed test. Estimate the number of simultaneous requests, target context lengths, batch sizes and acceptable response time. A card that feels fast for one prompt can become memory-bound or have poor tail latency when several requests share it.
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If you run models for a few hours a month, system price, noise and simplicity may dominate. A workstation used every day for long jobs should be evaluated for cooling, power efficiency, warranty and electricity as well as purchase price.
NVIDIA’s local-AI guidance identifies operating system, available GPU or unified memory, model size and workflow as core selection factors. See NVIDIA’s local-AI guide before choosing a product family.
2. Treat memory capacity as the first filter
VRAM determines whether a model and its runtime state can load at all. Compute performance matters only after the workload fits. Parameter count alone is not a VRAM specification: quantization, context length, key-value (KV) cache, batch size, temporary tensors and application overhead all change the requirement.
Build a memory estimate with headroom
Start with the exact model’s documented file size and runtime guidance. Add room for:
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- Model weights after the chosen precision or quantization.
- KV cache, which grows with context length and can become substantial for long prompts or many concurrent requests.
- Activation and workspace memory required by the framework or image pipeline.
- Batch size and the number of simultaneous requests.
- Operating-system and application overhead, including other GPU programs.
Do not plan to use every last megabyte. Leave practical headroom so a longer prompt, a software update or a second process does not cause an out-of-memory failure. If a model barely fits, reducing context or concurrency may be necessary, and that can undermine the reason for buying the card.
Published examples show the scale, not universal minimums
AMD reports the following memory figures in its own specified tests. They illustrate how requirements can exceed a simple parameter-count rule; they are not guaranteed minimums for every runtime or configuration.
| Workload or model | Published memory figure | How to interpret it |
|---|---|---|
| DeepSeek R1 Distill Qwen 32B Q6 | 28 GB | AMD test result; runtime, context and settings matter. |
| Mistral Small 3.1 24B Instruct 2503 Q8 | 27 GB | AMD test result for the stated quantization. |
| Flux.1 Schnell | 24 GB | AMD test result for the specified image workflow. |
| SD 3.5 Medium | 17 GB | AMD test result; resolution and pipeline components affect use. |
These figures come from the AMD Radeon AI PRO product page and should be treated as vendor-published examples, not a sizing table that applies to all cards.
Unified memory is different from dedicated VRAM
Some compact systems use unified memory shared by the CPU and GPU. NVIDIA lists RTX Spark and DGX Spark systems with up to 128 GB of unified memory. That can make larger models possible in a small system, but the memory is shared, so CPU applications, the operating system and the AI workload compete for it. Compare the complete system’s memory architecture and bandwidth rather than equating unified memory with a discrete card’s dedicated VRAM.
3. Match the software stack to the exact GPU
Compatibility is a purchasing requirement, not a detail to solve later. Confirm the operating system, driver branch, framework, inference server and application support the exact GPU model and version you plan to buy.
NVIDIA and CUDA
For NVIDIA hardware, use the official CUDA GPU Compute Capability table to identify the card’s compute capability. Then check the version requirements of your framework and application. A card can be listed as CUDA-capable yet still lack support in a particular release, precompiled package or optimized kernel.
AMD and ROCm
AMD support depends on the ROCm release, operating system, GPU and framework combination. Consult the live ROCm compatibility matrix for the current release before purchase. The matrix is version-specific, so a configuration supported today may require a different driver or framework after an upgrade.
Check the application, not just the vendor ecosystem
Ask these questions for every important tool:
- Does it support the GPU backend you intend to use?
- Does it provide a build or container for your operating system?
- Are the required features—such as Flash Attention, quantization formats, image extensions or multi-GPU operation—available on that backend?
- Is support experimental, and are there known limitations for your model?
“CUDA versus ROCm” is too broad to be a buying rule. The useful test is whether your exact application, release, operating system and card work together with the features you need.
4. Compare performance under your real workload
Generic gaming rankings or theoretical compute figures do not answer a local-AI buying question. Use measurements that match the job.
Metrics by workload
| Workload | Useful measurements | Test conditions to hold constant |
|---|---|---|
| Interactive language inference | Time to first token and tokens per second | Model, quantization, context length, prompt and generation length |
| Multi-user serving | Aggregate throughput and p95/p99 latency | Concurrent users, batching, context distribution and server settings |
| Image generation | Seconds per image and images per minute | Pipeline, resolution, steps, batch size and control modules |
| Fine-tuning or training | Steps per second, total time and stability | Dataset, precision, batch size, framework, checkpointing and validation |
How to read a current benchmark
A January 14, 2026 arXiv preprint by Jonathan Knoop and Hendrik Holtmann tested NVIDIA RTX 5060 Ti, 5070 Ti and 5090 cards across four open-weight models and 79 configurations. It varied quantization, context lengths from 8k to 64k, and RAG, agentic-serving and high-concurrency API workloads. In comparable tested workloads, the RTX 5090 delivered 3.5–4.6 times the throughput of the RTX 5060 Ti, while lower-cost cards had the highest throughput per dollar in some API tests.
Those results apply to the models, software and workload definitions in that preprint, not to every local-AI task. Read the complete preprint and record the tested model, quantization, context, concurrency and measurement before using a result in a purchase decision.
Vendor benchmarks need configuration labels
AMD’s comparisons of its Radeon AI PRO R9700 with an RTX 5080 used LM Studio 0.3.15, Vulkan llama.cpp 1.28 for AMD and CUDA 12 llama.cpp 1.30 with Flash Attention for NVIDIA on a specified Windows 11 Pro system. Other AMD value tests used a separate Threadripper PRO and Ubuntu configuration with ROCm against an RTX 4500 Blackwell. These are vendor tests under different configurations; do not combine their numbers into an independent ranking. Use them as evidence for the stated setup and then reproduce the critical workload on your own stack when possible.
5. Understand the current product classes
Product families provide a starting point, but the memory and support of the exact model still decide suitability.
| Product class | Published memory or role | Who it can suit | Important check |
|---|---|---|---|
| NVIDIA GeForce RTX | 6–32 GB VRAM for developing and testing small AI models, according to NVIDIA | Single-system users running smaller models and mainstream tools | Choose the exact VRAM capacity and verify CUDA/application support. |
| NVIDIA RTX PRO | 16–96 GB VRAM for developing and testing larger AI models, according to NVIDIA | Workstation users needing more capacity or professional features | Compare the complete workstation cost, thermals and software support. |
| NVIDIA RTX Spark and DGX Spark | Up to 128 GB unified memory, according to NVIDIA | Users who prefer a compact integrated local-AI system | Account for shared memory, bandwidth and limited upgrade paths. |
| AMD Radeon AI PRO R9700 | 32 GB VRAM; AMD markets it for local inference and development | Users whose applications support the AMD stack and need high capacity | Verify the current ROCm matrix, OS and application features. |
The memory ranges above are vendor product guidance. They do not promise that every model in a parameter class will run well or that every application supports every card.
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6. Price the whole system, not just the GPU
Exact street prices, inventory and product revisions change quickly, so a responsible comparison needs a current quote for the card and the rest of the system. Build a total-cost worksheet with these categories:
- GPU: Include the exact board, memory capacity and warranty.
- Power supply: Check rated capacity, connector type and whether the unit has enough margin for sustained loads.
- Case and cooling: Confirm card length, slot thickness, airflow and fan noise. A high-power card can require additional fans or a larger case.
- Platform: Account for motherboard slots, CPU performance, system RAM and storage. Data loading and preprocessing can bottleneck a powerful GPU.
- Operating electricity: Estimate hours of use, average system wattage and your utility rate. Sustained training or serving can make electricity material over the system’s life.
- Opportunity cost: Compare a discrete-card upgrade with a complete compact workstation if the existing case, power supply or motherboard would all need replacement.
- Multi-GPU expansion: Budget for additional power, physical space, cooling and software complexity. Two smaller cards do not automatically behave like one larger-memory card; VRAM is usually not pooled transparently by an application.
Do not use a lower purchase price to justify a card that cannot fit your model. Paying for cloud access, replacing a power supply or reselling an unsuitable GPU can erase the initial saving.
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7. A practical buying procedure
- List the workloads. Name each model, application, image pipeline or training job. Mark whether it is inference, fine-tuning, training, generation or serving.
- Record operating conditions. Specify operating system, context length, batch size, concurrency, target latency and whether use is occasional or sustained.
- Estimate memory. Use documented model and runtime figures, add KV cache and application overhead, and reserve headroom.
- Eliminate incompatible cards. Check the exact GPU, driver, framework and application support in the CUDA or ROCm documentation and the application’s own requirements.
- Shortlist by capacity. Compare cards that can load the workload without aggressive compromises to context, batch size or concurrency.
- Collect relevant benchmarks. Prefer measurements using your model and settings. If unavailable, choose results with the closest context, quantization and concurrency.
- Calculate total system cost. Include power delivery, cooling, case changes, electricity and any required operating-system or software changes.
- Test before committing when possible. Run a representative prompt, image batch or fine-tuning step and monitor VRAM, speed, temperature and stability.
- Document the configuration. Record driver, framework, model, quantization and settings so a future update can be compared fairly.
8. Common mistakes and how to correct them
Buying on compute numbers alone
Symptom: A card has impressive theoretical performance but the model will not load or frequently runs out of memory. Fix: Make capacity and software compatibility pass/fail gates before comparing speed.
Using parameter count as a memory guarantee
Symptom: A model described as “small enough” fails at a long context or with several users. Fix: Include quantization, KV cache, workspace, batch and concurrency in the estimate, with headroom.
Copying a benchmark without its conditions
Symptom: Your tokens-per-second result is far below a published number. Fix: Match model, quantization, context, backend, driver, batch and prompt; otherwise treat the result as directional only.
Assuming support is permanent
Symptom: A driver or framework update breaks an optimized path. Fix: Check release-specific compatibility, keep a known-good environment and update deliberately rather than changing the whole stack during an important project.
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Ignoring sustained thermals
Symptom: Short tests look fast, but long jobs throttle, become noisy or destabilize the system. Fix: Test for the expected job duration and verify airflow, power margin and cooling.
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Expecting two cards to solve a capacity problem automatically
Symptom: Two GPUs are installed, but the application still cannot load a model or requires complex sharding. Fix: Confirm explicit multi-GPU support and memory-partitioning behavior in the runtime before buying a second card.
9. Match the purchase to a reader profile
Small-model developer
Prioritize dependable software support, adequate VRAM for the models and contexts you use, and a system that fits your existing power supply and case. NVIDIA describes GeForce RTX cards in the 6–32 GB range as a path for developing and testing small AI models; select within that range only after checking your model’s actual memory requirement.
Larger-model workstation user
If your models need more memory, NVIDIA positions RTX PRO products in a 16–96 GB range for larger-model development. Compare the workstation’s total cost, cooling, warranty and application support rather than treating the professional label as a performance guarantee.
Compact-system buyer
A Spark or DGX Spark system can be attractive when space, integration and unified memory matter more than adding cards to a tower. NVIDIA lists up to 128 GB of unified memory for these systems. Evaluate shared-memory bandwidth, thermals and upgradeability before comparing it with a discrete GPU.
AMD-stack buyer
The 32 GB Radeon AI PRO R9700 is a high-capacity discrete option for users whose exact operating system, ROCm release and applications support it. Verify the live compatibility matrix and reproduce a representative workload; memory capacity alone does not establish application performance.
Multi-user service operator
Choose for throughput and tail latency at your planned concurrency, then verify that the card has enough memory for the largest simultaneous context and batch. A lower-cost card may deliver better throughput per dollar in a particular serving workload, while a larger card may reduce operational complexity by avoiding aggressive batching or model splitting.
10. A final decision checklist
- Have you named the exact models and applications?
- Have you separated inference, fine-tuning, training, image generation and serving?
- Does the model plus context, KV cache, batch and overhead fit with headroom?
- Does the exact GPU have support in your operating system, driver, framework and application?
- Are benchmark conditions comparable to your workload?
- Can your case, motherboard, power supply and cooling handle the card?
- Have you included electricity and likely system upgrades in the total cost?
- Would a compact unified-memory system or a professional card be cheaper overall than rebuilding a desktop?
- Do you have a rollback plan for driver or framework changes?
The best GPU for local AI is therefore the least expensive configuration that reliably supports your required models, memory headroom, software features and performance target. If two options both pass those tests, compare sustained workload speed, power use, noise, warranty and the cost of the complete system—not a generic ranking.
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