First-class
NVIDIA (desktop / pro)
CUDA is the default path: Ollama, llama.cpp (CUDA), vLLM, SGLang. Best docs, GGUF ecosystem, and agent tooling. Prefer NVIDIA when the budget is for a coding desk GPU.
Local coding
Bandas de hardware
The lock is memory, not TFLOPS. Buy the band that fits weights plus KV for your real context—or keep paying the API.
Protocol
- VRAM / unified memory is the lock—not TFLOPS.
- Weights fitting the card ≠ target context fitting (KV tax).
- Local token stickers are ~$0; you still pay electricity and depreciation.
Memory bands
Accent column = USD reference range for the memory band (mid in parentheses).
| Band | USD ref (band) | Typical class | Max params (Q4 class) | Workloads |
|---|---|---|---|---|
| 16GB | $250–$750 ($450) | RTX 4060 / 5060 Ti 16GB class / Mac 16GB unified | 8B | Tab complete / short chat |
| 24GB | $1.4k–$3.2k ($2.2k) | RTX 4090 24GB / Mac 24–32GB unified | 32B | Tab complete / short chat · Repo coding agent |
| 32GB | $2k–$5.5k ($3.8k) | RTX 5090 32GB class / Mac 32–36GB unified | 40B | Tab complete / short chat · Repo coding agent · Long-context review |
| 48GB | $2.5k–$7k ($4.5k) | 48GB workstation / dual-24 class / Mac 64GB unified | 70B | Tab complete / short chat · Repo coding agent · Long-context review |
| 80GB | $6k–$14k ($9k) | 80GB-class pro card / Mac Studio 96–128GB | 120B | Tab complete / short chat · Repo coding agent · Long-context review |
| 128GB | $8k–$18k ($12k) | Mac Studio 128–192GB / multi-GPU desk | 180B | Tab complete / short chat · Repo coding agent · Long-context review |
| 512GB | $18k–$45k ($28k) | Mac Studio 512GB / multi-GPU rack | 480B | Tab complete / short chat · Repo coding agent · Long-context review |
Representative SKU reference prices (USD)
GPU-card or device-class streets. Mac rows are whole-device unified memory, not a discrete GPU sticker.
| Band | SKU / class | USD ref |
|---|---|---|
| 16GB | RTX 4060 Ti / 5060 Ti 16GB class (card) | $300–$550 |
| 16GB | Used RTX 3090 / 3080 Ti class (often 12–24GB) | $400–$900 |
| 24GB | RTX 4090 24GB (used/new street) | $1.5k–$2.8k |
| 24GB | Used dual-12GB / single-24GB workstation class | $1.2k–$2.5k |
| 32GB | RTX 5090 32GB (MSRP vs street — volatile) | $2k–$5.1k |
| 48GB | Dual used RTX 3090 (2×24GB) system delta | $2k–$3.5k |
| 48GB | Mac Studio / Pro ~64GB unified (device, not GPU-only) | $2.5k–$4.5k |
| 80GB | Pro / datacenter 80GB-class (A100 80 / H100 80 street) | $8k–$25k |
| 80GB | Mac Studio Ultra ~96–128GB unified (device) | $5k–$9k |
| 128GB | Mac Studio high unified config (device) | $7k–$15k |
| 128GB | Multi-GPU desk (2×32–48GB class) | $7k–$16k |
| 512GB | Mac Studio max unified / mini-cluster class | $15k–$40k |
NVIDIA · AMD · Intel · Apple
Local coding inference is CUDA/Metal-first. AMD and Intel can work in limited setups—read the “why” before you buy for that reason alone.
Limited support
AMD (Radeon / Instinct)
Usable, not first-class. ROCm coverage is uneven by card/OS; many coding apps and GGUF paths expect CUDA. llama.cpp Vulkan/ROCm builds can run, but expect slower bring-up, fewer “it just works” guides, and weaker vLLM/SGLang desk stories. Buy AMD only if you already own the card or accept tinkering—not as the cheapest path to a Cursor-like local agent.
Limited support
Intel (Arc / Xe / discrete)
Experimental for serious local coding agents. SYCL/oneAPI and llama.cpp backends exist, but Arc VRAM ceilings, driver maturity, and missing CUDA-centric tooling make Intel a poor primary buy for SWE-style agents. Fine for light experiments if the GPU is already in the machine; do not plan a coding workstation around Arc alone.
First-class (unified memory)
Apple Silicon
Not a discrete GPU vendor path—unified memory + MLX (or llama.cpp/Ollama). Expensive $/GB but simple ops. See band and SKU tables for device-class USD ranges.
TCO vs API
Illustrative break-even: monthly capex (3-year) plus electricity versus one 100K+20K coding job on OpenRouter.
| Band | Mid capex (class) | Break-even jobs / month |
|---|---|---|
| 16GB | $450 | 1112 |
| 24GB | $2.2k | 4970 |
| 32GB | $3.8k | 8497 |
| 48GB | $4.5k | 10040 |
| 80GB | $9k | 19961 |
| 128GB | $12k | 26575 |
| 512GB | $28k | 61848 |
Do not buy
- ≤16GB laptop forcing 70B weights.
- 24GB card expecting 480B-class coder downloads.
- Personal SWE buying H100 / cluster class for tab-complete.
Assumptions and sources
Hardware bands are classes, not street quotes. VRAM estimates use curated GGUF footprints plus size-class KV factors. API job cost uses OpenRouter primary listing when present. choose-ai-infrastructure-guide covers API hosts—not desk GPUs.
Hardware USD figures are curated mid-2026 reference ranges for planning. Verify current street price before buying.