CALIBRATED 2026-07-27 · REC 003
Local AI Frontier

FIELD GUIDE

The local-AI GPU buyer's guide

How to choose between NVIDIA, AMD, and Intel Arc for local LLM workloads — by workload, not brand loyalty.

Buying a GPU for local AI is not the same as buying one for gaming. The bottleneck is VRAM capacity and memory bandwidth at sustained load, not peak frame rate. This guide walks the decision by workload, with numbers pulled from our lab benchmarks.

The decision, in one question

What is the largest model you want to run comfortably, and how latency-sensitive is it? Everything else — brand, peak TFLOPs, ray-tracing cores — is secondary.

Three honest starting points

Based on our tested builds and benchmarks:

  • < $1,500 budget, 7B–13B models, occasional 30B at Q4: Intel Arc B580 12GB on Vulkan. Best $/tok we've measured. raw data
  • ~$2,200 budget, 30B daily, 70B at Q3: AMD RX 7900 XTX 24GB on Vulkan. Single-card simplicity, 24GB fits the sweet spot.
  • ~$3,500 budget, latency-critical agent loops: NVIDIA RTX 4090 24GB on CUDA. Still the speed leader at this tier. raw data

Brand-by-brand, honestly

NVIDIA

Still the speed leader and the only mature CUDA ecosystem. If your workload is latency-sensitive (agent loops, voice, real-time coding), CUDA's lead is real — our benchmarks show ~35% faster generation at the same 24GB tier. The cost is money and power: NVIDIA cards are more expensive per GB of VRAM and draw more watts.

AMD

Best raw VRAM-per-dollar at the mid and high tiers. ROCm works but is finicky on consumer cards; Vulkan via llama.cpp is currently the more consistent path for RDNA3–4 hardware. Multi-GPU AMD configs win for throughput workloads but add complexity (tensor-split quirks, driver attention).

Intel Arc

The price-performance surprise. Battlemage (B-series) on Vulkan lands within ~12% of equivalently-priced AMD/NVIDIA on generation speed for code workloads, often at lower system cost. Critical caveat: SYCL produces garbage output on Battlemage — use Vulkan. If you can accept that constraint, Arc is the budget winner.

What to ignore

  • Peak TFLOPs — marketing number; irrelevant to sustained LLM inference.
  • Ray tracing cores — unused by LLM runtimes.
  • "AI performance" on the box — usually FP16 matrix-multiply marketing, not what llama.cpp measures.
  • Brand-tier rankings — the right GPU depends on your workload, full stop.

Before you buy

  1. Use Model Fit to estimate what your target model needs.
  2. Check our benchmarks for your platform.
  3. Read the matching build recipe for parts and total cost.
  4. Confirm PSU, case clearance, and cooling — local AI sustains load for hours, not seconds.