Can Apple MacBook Pro 14" M2 Pro 16GB (used) run Llama, Qwen & DeepSeek? 254 models that fit
254 of the 396 models in the Spanvero catalog fit Apple MacBook Pro 14" M2 Pro 16GB (used)'s 16 GB unified memory (at a sensible quant, 16k context). For each: run it locally ($0 compute + electricity), rent an equivalent GPU ($0 markup, as of 2026-07-02), or pay per-token via your own API key (as of 2026-07-06).
Three honest ways to run each model on Apple MacBook Pro 14" M2 Pro 16GB (used)
Run it locally: $0 in compute — you pay only electricity (~96 W under load on this Mac). Local is real money, never a fake "$0".
Rent an equivalent GPU: from a $0-markup vendor rate (as of 2026-07-02) — you rent on your own account and pay the vendor directly; we never resell compute.
Skip the box: run the same model through your own API key, paying per million tokens (prices as of 2026-07-06).
What fits Apple MacBook Pro 14" M2 Pro 16GB (used) (16 GB unified memory)
254 of the 396 notable models in the Spanvero catalog fit Apple MacBook Pro 14" M2 Pro 16GB (used) at a sensible quant (context capped at 16k for the estimate). Most capable first:
NVIDIA Nemotron 3 Nano 30B A3B NVFP4 (nvidia, 18.2B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.255/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.25/1M via your own API key (size estimate).
Qwen3 30B A3B NVFP4 (RedHatAI, 17.5B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.2472/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.24/1M via your own API key (size estimate).
deepseek moe 16b base (deepseek-ai, 16.4B) — needs ~12 GB at Q4_K_M: run it locally for $0 compute + ~$0.2347/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.23/1M via your own API key (size estimate).
deepseek moe 16b chat (deepseek-ai, 16.4B) — needs ~12 GB at Q4_K_M: run it locally for $0 compute + ~$0.2347/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.23/1M via your own API key (size estimate).
LLaDA2.0 mini (inclusionAI, 16.3B) — needs ~12 GB at Q4_K_M: run it locally for $0 compute + ~$0.2335/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.23/1M via your own API key (size estimate).
DeepSeek-Coder-V2-Lite Instruct (DeepSeek, 15.7B) — needs ~11 GB at Q4_K_M: run it locally for $0 compute + ~$0.2266/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.23/1M via your own API key (size estimate).
Qwen3 30B A3B NVFP4 (nvidia, 15.6B) — needs ~12 GB at Q4_K_M: run it locally for $0 compute + ~$0.2255/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
Qwen2.5 Coder 14B Instruct (Qwen, 14.8B) — needs ~14 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
Qwen2.5 14B Instruct (Qwen, 14.8B) — needs ~14 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
Qwen3 14B (Qwen, 14.8B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.17/1M via your own API key.
Qwen2.5 14B Instruct (unsloth, 14.8B) — needs ~14 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
phi 4 quantized.w4a16 (RedHatAI, 14.8B) — needs ~14 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
Qwen3 14B Base (Qwen, 14.8B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.2162/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
HyperCLOVAX SEED Think 14B (naver-hyperclovax, 14.7B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.215/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.22/1M via your own API key (size estimate).
Qwen1.5 MoE A2.7B (Qwen, 14.3B) — needs ~12 GB at Q4_K_M: run it locally for $0 compute + ~$0.2103/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.21/1M via your own API key (size estimate).
Phi-4 (Microsoft, 14B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.2068/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.11/1M via your own API key.
HarmBench Llama 2 13b cls (cais, 13B) — needs ~11 GB at Q4_K_M: run it locally for $0 compute + ~$0.1949/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.20/1M via your own API key (size estimate).
Mellum2 12B A2.5B Thinking (JetBrains, 12.1B) — needs ~9 GB at Q4_K_M: run it locally for $0 compute + ~$0.184/1M in electricity, rent NVIDIA RTX 3060 12GB from $0.06/hr ($0 markup), or ~$0.20/1M via your own API key (size estimate).
Mellum2 12B A2.5B Base (JetBrains, 12.1B) — needs ~9 GB at Q4_K_M: run it locally for $0 compute + ~$0.184/1M in electricity, rent NVIDIA RTX 3060 12GB from $0.06/hr ($0 markup), or ~$0.20/1M via your own API key (size estimate).
Gemma 3 12B (Google, 12B) — needs ~13 GB at Q4_K_M: run it locally for $0 compute + ~$0.1828/1M in electricity, rent 2× NVIDIA RTX 3060 12GB from $0.12/hr ($0 markup), or ~$0.10/1M via your own API key.
The honest cost of owning Apple MacBook Pro 14" M2 Pro 16GB (used)
Street price $1,250.00 (as of 2026-07-08; unverified street-price estimate — being sourced) — amortized over 3 years that's ~$1.1416/day whether or not you're generating.
Electricity: ~96 W under sustained inference at $0.1883/kWh (EIA Electric Power Monthly Table 5.6.A — U.S. residential average, Apr 2026, as of 2026-07-02) — the per-1M-token figures above already include this at each model's speed.
Straight talk: for the small models a 16 GB unified memory box runs, hosted APIs are often cheaper per token. Own local for privacy, offline use, and unlimited runs — not to save money on tokens.
Too big for Apple MacBook Pro 14" M2 Pro 16GB (used) — rent or use an API instead
These need more than the 16 GB unified memory on this Mac. Closest first — you can still run them on a rented GPU ($0 markup) or via your own API key:
gpt-oss-20b (OpenAI, 21B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.08/1M via your own API key.
gpt oss 20b BF16 (unsloth, 20.9B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.27/1M via your own API key (size estimate).
Qwen3 32B NVFP4 (nvidia, 17.2B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.24/1M via your own API key (size estimate).
DeepSeek V2 Lite Chat (deepseek-ai, 15.7B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.23/1M via your own API key (size estimate).
DeepSeek V2 Lite (deepseek-ai, 15.7B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.23/1M via your own API key (size estimate).
NVIDIA Nemotron Nano 12B v2 (nvidia, 12.3B) — needs ~15 GB; rent 2× NVIDIA RTX 3060 12GB from $0.12/hr, or ~$0.20/1M via your own API key (size estimate).
A short email of real AI price moves, straight from the daily log — no hype. We're collecting the list now; the first issue goes out when it opens. Unsubscribe with one click.