Explore builds
STARTER BUILD / RigAtlas starter

Core i5-12400F + Arc B580 PC Build.

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

An LGA1700 and DDR4 alternative with an Arc B580 card. Enable Resizable BAR as required by the GPU guidance and check performance in your own games and applications. 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

Intel value · Arc B580 12 GB

8 / 8 core parts
COMPONENTYOUR SELECTIONRECORDED PRICE
Processor
LGA1700 · DDR4/DDR5
Checking price…
Not recorded
Motherboard
LGA1700 · DDR4 · micro-ATX
Checking price…
Not recorded
Graphics card
12 GB GDDR6
Checking price…
Not recorded
Memory
DDR4 · 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
Mini-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 memory12 GB
Selected graphics cardIntel Arc B580 Challenger 12GB 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 Intel GPU support in the exact application and its XPU or Vulkan backend.

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 Intel GPU support in the exact application and its XPU or Vulkan backend.
  • 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 Intel LGA1700 and DDR4 alternative using an Arc B580. It is intended to make platform cost and Arc-specific setup requirements visible in one parts list.

Why these components are together

The Core i5-12400F has no integrated display fallback, so the Arc card is required in this selection. The PRIME B660M-A D4 explicitly uses DDR4.

The 32 GB DDR4 kit and 1 TB NV3 provide a capacity baseline that can be compared against your games or applications. The B580's 12 GB graphics memory is a separate resource.

The Q300L, Assassin X and A650BN create a conventional micro-ATX layout. Inspect the exact ASRock card dimensions and power guidance when evaluating that enclosure and PSU.

Tradeoffs and checks before ordering

  • Intel's Arc guidance calls for Resizable BAR and the appropriate UEFI configuration for optimal performance; confirm support before buying.
  • Read application support and representative game tests for the exact Arc generation rather than assuming all graphics brands behave alike.
  • An Intel Core Ultra LGA1851 processor is not a replacement for an LGA1700 CPU in this board.

What to change first

Resolve firmware and driver setup before diagnosing the hardware as too slow. If a workload still needs a change, compare a replacement GPU or complete CPU platform with the cost of any supporting parts.

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