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The TokenByte GPU shortlist
Start with the model or workflow, not the card name. VRAM decides whether the job fits. Memory bandwidth, software support, power, cooling, warranty, and purchase price decide whether the card is pleasant to own.
Smaller local models, transcription, basic image work, and proof-of-workflow builds.
More headroom without committing the whole system budget to one component.
Serious inference, creator workflows, and better room for concurrent tools.
Five buying paths, ranked by fit
| Path | Best for | What breaks first | TokenByte verdict |
|---|---|---|---|
| 12GB starter | Learning, smaller models, light creator work | Large models, long context, complex pipelines | Buy only when the low price is the advantage. |
| 16GB new card | Balanced first AI PC, lower power, warranty | 24GB-class model fit and multi-tool headroom | The sensible new-build floor for a committed beginner. |
| Used RTX 3090 24GB | Maximum VRAM per dollar | Heat, power, age, seller risk | High leverage when condition and returns check out. |
| RTX 4090 24GB | Fast mature CUDA workstation | Price; no capacity gain over a 3090 | Pay for speed and predictability, not more VRAM. |
| RTX 5090 32GB | Single-card work that exceeds 24GB | Total cost, power, case clearance | Excellent hardware; rational only when 32GB solves a real failure. |
Who should buy—and who should wait
You can name the model, context, batch, precision, or pipeline that does not fit or runs too slowly.
Use existing hardware or a small cloud test first. A measurement is worth more than a fantasy build.
Compare Apple Silicon, GB10 systems, and high-memory AI mini-workstations before building a tower.
Do not know your tier?
Answer four questions about workload, budget, privacy, and noise. The picker will route you to the first setup worth investigating.
A GPU is not the whole AI computer
- System RAM: 64GB is a practical workstation default; heavier multitasking and offload can justify more.
- Storage: active models, caches, datasets, and outputs can make a 1TB drive feel small quickly.
- Power and thermals: sustained AI work exposes weak PSUs, tight cases, poor airflow, and careless cabling.
- Software: verify CUDA, framework, driver, operating-system, and model support before paying for hardware.
- Returns: a high-ticket card without a credible return path is not a bargain.
When a discrete GPU is the wrong answer
High-unified-memory systems are becoming a serious alternative. NVIDIA's GB10/DGX Spark class targets compact 128GB desktop AI, Apple systems trade CUDA compatibility for quiet operation and large unified-memory configurations, and AMD Ryzen AI Max machines bring high shared-memory ceilings to x86 mini-workstations. Software fit and memory bandwidth still matter; “unified memory” is not automatically faster than a discrete GPU.
GPU buying FAQ
How much VRAM do I need for local AI?
Start at 12GB for smaller models and learning, use 16GB for a more flexible new build, target 24GB for serious local model and creator workflows, and pay for 32GB only when the workload genuinely crosses the 24GB ceiling.
Is a used RTX 3090 still good for local AI?
A clean used RTX 3090 can remain a strong 24GB value, but condition, return protection, power, heat, and case clearance are part of the purchase.
Should I buy an RTX 4090 or RTX 5090 for local AI?
Choose the RTX 4090 when 24GB fits and speed matters. Choose the RTX 5090 when a verified workload needs more than 24GB on one card and the total system cost is justified.
Do I need an NVIDIA GPU for local AI?
No, but NVIDIA remains the lowest-friction choice for many CUDA-dependent tools. Apple Silicon and high-unified-memory AMD systems can be better for quiet operation or large-memory inference when their software support matches the workload.