Explore builds
STARTER BUILD / RigAtlas starter

Ryzen 5 7500F + RTX 5060 PC Build.

Reference imageImages are for reference only. Refer to the linked store for actual product images.
ABOUT THIS BUILD

A six-core AM5 platform with an 8 GB GeForce card. The processor has no integrated graphics; keep the graphics card in the configuration. This USD budget is an illustrative planning target, not a verified parts total or a retailer quote. Check current local prices, BIOS support and mounting clearances. Images are for reference only. Refer to the linked store for actual product images.

8 componentsBudget in USD
CONFIGURATION

Entry AM5 · GeForce RTX 5060

8 / 8 core parts
COMPONENTYOUR SELECTIONRECORDED PRICE
Processor
AM5 · DDR5
Checking price…
Not recorded
Motherboard
AM5 · DDR5 · micro-ATX
Checking price…
Not recorded
Graphics card
8 GB GDDR7
Checking price…
Not recorded
Memory
DDR5 · UDIMM
Checking price…
Not recorded
Storage
M.2 2280
Checking price…
Not recorded
Power supply
ATX
Checking price…
Not recorded
CPU cooler
Single tower
Checking price…
Not recorded
Case
Mid-tower
Checking price…
Not recorded
Recorded prices are entered by you in USD. Retailer prices, tax and delivery may differ.
AI & YOUR BUILD

What AI work can this PC handle?

Explore cloud use, local language models and image generation with the parts you selected. Memory estimates are not speed benchmarks.

System memory32 GB
Dedicated graphics memory8 GB
Selected graphics cardGeForce RTX 5060 8G VENTUS 2X OC

Cloud AI

Inference runs on the service’s servers.

A larger local GPU is not required just to chat with a cloud model or request cloud-generated images. Check the service account, availability in your region, internet connection and supported browser or client.

Local editing, 3D work and running several apps still use your PC’s resources. This does not guarantee a service’s response time.

How cloud inference works

Local image generation

Choose the exact model and workflow first.

Check the exact GPU, driver and CUDA support in your chosen application.

Image size, batch size, model components and extra nodes change memory use. CPU offloading can reduce VRAM demand but may be slow. Video generation, training and fine-tuning require separate assessments.

ComfyUI hardware requirementsModel memory and offloading

Local language models

Change the assumptions to explore the memory tradeoff. These are model sizes, not recommendations for a specific model.

Weights may fit; full inference still needs verification.

Theoretical weight storage only: 3.7 GiB · 8B / 4-bit

Actual use is higher: quantization metadata, unquantized tensors, KV cache, compute buffers and the runtime also need memory. Total installed memory is not the same as free memory.

Weight storage scenarios at the selected precision
ParametersWeight lower boundCompared with selected VRAM
3B1.4 GiBWeights only; overhead not included
8B3.7 GiBWeights only; overhead not included
14B6.5 GiBWeights only; overhead not included
32B14.9 GiBNot enough for weights alone
70B32.6 GiBNot enough for weights alone
  • Check the exact GPU, driver and CUDA support in your chosen application.
  • Longer context increases KV-cache demand. A weights-only fit does not establish whether your selected context or concurrent requests will fit.
  • Check CPU instruction support, the exact OS version, drivers and available SSD space for model downloads. LM Studio on Windows x64 requires AVX2; this catalog does not verify instruction sets.
Method, limits and how to verify

Weight lower bound = total parameters × bits ÷ 8. B means one billion parameters; GiB means 2³⁰ bytes. All parameters are counted, including inactive experts in a mixture-of-experts model. Nominal catalog RAM and VRAM capacities are used as GiB for this planning comparison.

Shared system memory is not added to dedicated VRAM. RAM kit capacity is counted once. Already-owned parts still count as installed hardware. No throughput or “smooth performance” claim is made.

After assembly, use the actual model file and context setting with the runtime’s resource estimator, then measure response speed and memory use in your workload.

LM Studio resource estimation

Weight estimates · Context and memory · GPU runtime support · Application requirements

Selected GPU manufacturer specifications · 2026-09-10

AI documentation checked: 2026-09-19
THE CHOICES BEHIND THIS PARTS LIST

Who this build is for

An AM5 entry point using a Ryzen 5 7500F and a GeForce RTX 5060, with a larger tower case around the micro-ATX motherboard.

Why these components are together

The 7500F requires the discrete graphics card for a display path. The B650M Pro RS and DDR5 kit establish an AM5 platform that must be checked against the board support list.

The 32 GB memory kit and 1 TB SSD are separate from the GPU's 8 GB VRAM. Choose texture settings and application workloads with the exact card's capabilities in mind.

The AIR 903 BASE leaves the layout less space-constrained than a small enclosure. The RM650e uses its own approved modular cables; keep them identified with the PSU.

Tradeoffs and checks before ordering

  • An AM5 label does not confirm the shipped BIOS or every future CPU upgrade.
  • An 8 GB GPU is not equivalent to a larger-memory variant simply because both share a brand or family name.
  • The cooler mounting kit and memory clearance still need to be verified.

What to change first

Change the GPU only when a measured workload or required software feature justifies it. Then recheck PSU output, exact connectors and case clearance rather than treating the existing 650 W unit as universal.

This is an editorial parts-list explanation. The planning target is not a current retailer total, and this configuration has not been benchmarked by RigAtlas.

Component specification sources