Choose an AI GPU by first checking whether its usable VRAM can run your specific model and workload; then compare memory bandwidth, software support, system requirements, and the cost of producing useful output. Peak specifications alone cannot tell you which GPU is the better choice, especially when comparing consumer cards with workstation or data-center accelerators.
Start with the workload, not the GPU
“AI workloads” covers tasks with very different hardware demands. Local inference, image generation, model development, fine-tuning, model training, and production inference may call for different amounts of memory, throughput, software support, and system capacity. Before comparing GPUs, write down what you need the machine to do.
- The model and model format you plan to use.
- The precision or quantization setting, context length, and batch size.
- For a service, the number of concurrent users and the latency or throughput target.
- Whether the workload must run on one GPU or can use multiple GPUs.
- The framework and serving software you intend to run.
These details matter because there is no universal VRAM threshold that makes a GPU suitable for every AI task. The same model can have different memory requirements under different formats and runtime configurations.
Size VRAM for the complete workload
GPU memory is a feasibility constraint: if the required model and runtime allocations do not fit in usable VRAM, the workload may not run as intended. Model weights are only part of the requirement. Context length and KV cache, activations, batch size, and serving configuration can also affect memory use. There is no universal sizing formula established for these factors, so check memory use with the actual model and software configuration you expect to run.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesVendor examples show why a single “GB per model size” rule is unreliable. AMD reports that its Radeon AI PRO R9700 system used 28GB for DeepSeek R1 Distill Qwen 32B Q6 and 27GB for Mistral Small 3.1 24B Instruct 2503 Q8. These are AMD-reported results for those models and configurations, not minimum requirements for all users or systems. AMD’s stated test setup was a Ryzen 9 7900X, 32GB DDR5, Windows 11 Pro 24H2, Adrenalin 25.6.1 RC, and ComfyUI with PyTorch 2.4; AMD says results may vary. AMD’s Radeon AI PRO page provides the examples and setup.
Do not treat system RAM, shared graphics memory, and dedicated VRAM as interchangeable. AMD describes Variable Graphics Memory as a BIOS-level reallocation of system RAM to integrated graphics on supported Ryzen AI systems; that is not the same configuration as a discrete GPU with dedicated VRAM. See AMD’s Variable Graphics Memory and AI-model FAQ.
Choose precision and quantization with model behavior in mind
Lower-precision or quantized model formats can reduce memory use, but memory savings are not the only consideration: model behavior and performance can change too. In its FAQ discussing llama.cpp, AMD says Q6 is generally its suggested minimum for coding use in that context, while Q8 uses more memory and can carry a performance penalty. Treat this as AMD’s guidance for the described context, not a guarantee for every model, task, or software stack. Check the model-specific guidance and validate output quality for your own use case. AMD’s FAQ
Compare bandwidth only in context
VRAM capacity tells you how much data can fit; memory bandwidth describes how quickly data can move to and from GPU memory. Bandwidth can affect throughput, but a published bandwidth figure is not a workload benchmark. Software, model, precision, batch size, and system configuration all matter. Whenever possible, compare results for the same workload and software stack rather than using bandwidth alone to predict speed.
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For reference, NVIDIA lists the L4 at 300GB/s, H100 SXM at 3.35TB/s, and H100 NVL at 3.9TB/s. These are different product classes and configurations, not a controlled performance comparison. The specifications are published on NVIDIA’s L4 and H100 pages.
Use the GPU examples as starting points, not a universal ranking
The following specifications help distinguish local workstation options from data-center accelerators. Memory and bandwidth figures are per GPU; they should not be read as equivalent performance ratings. System power, where shown, is not directly comparable across product classes.
| GPU and class | Memory | Published bandwidth | Power figure | What to note |
|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 (consumer/local) | 32GB GDDR7 | Not stated on the cited product page. | 1000W required system power | Required system power is not the same measure as GPU TDP. NVIDIA RTX 5090 specifications |
| AMD Radeon AI PRO R9700 (workstation/local) | 32GB VRAM | Not stated on the cited product page. | Not stated on the cited product page. | AMD lists a $1,299 USD MSRP as of October 1, 2025; this is a dated MSRP, not a current street price. AMD Radeon AI PRO specifications and examples |
| NVIDIA L4 (data center, edge, and cloud) | 24GB | 300GB/s | 72W maximum TDP | A lower-power example; these specifications alone do not establish value for a particular workload. NVIDIA L4 specifications |
| NVIDIA H100 SXM (data center) | 80GB | 3.35TB/s | Configurable TDP up to 700W | Verify the exact configuration and interconnect in the system being considered. NVIDIA H100 specifications |
| NVIDIA H100 NVL (data center) | 94GB | 3.9TB/s | Configurable 350–400W | Different configuration from H100 SXM; do not assume their system characteristics are interchangeable. NVIDIA H100 specifications |
| NVIDIA H200 SXM (HGX data-center configuration) | 141GB HBM3e | 4.8TB/s | Not stated in the cited HGX component guide. | Per-GPU specification; consult the system documentation for system-level details. NVIDIA HGX component guide |
| NVIDIA B200 SXM (HGX data-center configuration) | 180GB HBM3e | Up to 8TB/s | Not stated in the cited HGX component guide. | Per-GPU specification; consult the system documentation for system-level details. NVIDIA HGX component guide |
Check the whole system and software stack
A GPU specification does not establish that a particular model will work well in your machine. Confirm that the exact framework, drivers, operating system, model-serving stack, and precision mode support your intended workload. For a workstation, also check the power supply, cooling, case clearance, available slots, and the cost of the rest of the system.
For multi-GPU setups, capacity on several devices does not automatically behave like one larger pool of usable VRAM. Scaling depends on the software, workload, interconnect, and server design. NVIDIA documents HGX configurations with four or eight GPUs and high-speed GPU-to-GPU links; check the specific system’s configuration rather than assuming aggregate capacity guarantees a given model will fit or scale. NVIDIA HGX component guide
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Compare cost by the work you can actually deliver
For a locally owned workstation
Do not compare cards using purchase price alone. Account for the GPU, the compatible rest of the system, power and cooling, and how often the machine will be used. Current retailer prices and availability are not established by the specifications cited here; check the market in your region before making a purchase decision. AMD’s $1,299 USD R9700 MSRP is specifically dated October 1, 2025, so it should not be treated as a current price.
For inference deployments
Measure cost against useful output under a defined model, precision, serving stack, throughput target, and quality requirement. NVIDIA’s H100 FAQ calls cost per token the key inference TCO metric. NVIDIA reports the following figures citing SemiAnalysis InferenceX benchmarks as of April 2026:
| GPU | Model and serving stack | Reported throughput | Reported cost |
|---|---|---|---|
| H100 | GPT-OSS-120B using vLLM | 66 TPS/user | Approximately $0.09 per million tokens |
| B200 | GPT-OSS-120B using TensorRT-LLM | 55 TPS/user | Approximately $0.02 per million tokens |
These are vendor-reported benchmark figures, not universal prices or a like-for-like comparison: the serving stacks and reported throughput differ. They cannot predict the cost of another model, service target, or deployment. See NVIDIA’s H100 product page and FAQ for the figures and attribution.
Quick Recap
A practical selection sequence
- Define the job: choose the model, format, precision, context length, batch size, concurrency, and latency or throughput target.
- Check memory feasibility: measure the chosen model and runtime in the intended software, leaving room for workload-specific allocations rather than sizing from model weight alone.
- Compare workload performance: use results from the same model and software stack where possible; treat bandwidth as one specification, not a speed verdict.
- Verify compatibility and system fit: confirm drivers and framework support, then check power, cooling, physical fit, and—in a multi-GPU system—the interconnect and software scaling behavior.
- Calculate delivered economics: for a workstation, include the full system and utilization; for inference, compare cost per useful output at the required quality and service level.
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.
