What AI can you actually run locally at every memory size, from 16GB to 1TB? I walk the whole local AI ladder (16GB, 32GB, 64GB, 128GB, 256GB, 512GB and 1TB) and show the best local LLM for each rung, what it scores, what the hardware costs, and which doublings buy a smarter model versus the same model with more bits.

Most "how much RAM for local AI" tier charts draw a smooth line. On the Artificial Analysis Intelligence Index it's a staircase: Qwen 3.8 27B scores 20.1 at 16, 32 and 64GB. GLM-5.3-Flash brings 27.8 at 128GB. DeepSeek V4.1 Flash brings 32.5 at 256GB, and the same 32.5 at 512GB with its fourth bit back. Then Kimi K3.1 Pro (46.0, weights promised for November 4) turns the 1TB rung into the biggest jump on the ladder.

The best local AI model for each memory size:
▸ 16GB: Qwen 3.8 27B at 3-bit on an RTX 5060 Ti 16GB. On a 16GB Mac mini, Gemma 4 12B
▸ 32GB: Qwen 3.8 27B at 4-bit with a 128K context ($1,299 Mac mini)
▸ 64GB: Qwen 3.8 27B at 8-bit with the full 256K context, plus room for multiple agents (M5 Pro Mac mini, $2,699)
▸ 128GB: GLM-5.3-Flash or Qwen 3.8 Flash-Next (Mac Studio M5 Max, NVIDIA DGX Spark, AMD Strix Halo)
▸ 256GB: DeepSeek V4.1 Flash at 3-bit (Mac Studio M5 Ultra, or two DGX Sparks)
▸ 512GB: DeepSeek V4.1 Flash at 4-bit. Two 256GB Mac Studios cost less than one 512GB
▸ 1TB: Kimi K3.1 Pro at 2-bit (711–861GB builds)

Also covered: the macOS GPU memory limit (one terminal command decides whether a Mac Studio runs the bigger model), DGX Spark vs Mac Studio prompt speed, running big MoE models on a PC with GPU + RAM + SSD (Strata), why renting a model runs the opposite way to buying hardware (total vs active parameters), and the speed ceiling at 1TB.

⏱️ Chapters
0:00 What AI you can run at every memory size
1:49 Why "more RAM = smarter AI" charts mislead
4:03 16GB: Qwen 3.8 27B vs Gemma 4 12B
5:42 32GB: Qwen 3.8 27B with 128K context
6:34 64GB: 8-bit, 256K context and multiple agents
8:24 128GB: Mac Studio M5 Max vs DGX Spark vs Strix Halo
11:07 256GB: DeepSeek V4.1 Flash on the M5 Ultra
13:38 512GB Mac Studio ($22,299) vs two 256GB
15:53 1TB: Kimi K3.1 Pro, the biggest jump
18:53 GPU + RAM + SSD: running big models on a PC
20:45 Renting vs owning: why the bill runs backwards
23:24 The speed ceiling nobody advertises
25:08 Who actually needs 1TB
26:29 The whole ladder in one minute
28:23 Rent first, then buy the smallest rung that fits

📺 Watch next
Every Way to Get 128GB VRAM for Local AI → https://youtu.be/NA9D49kQFuU
Every Way to Get 256GB of VRAM for Local AI → https://youtu.be/JPHT9AvoPSA
Every Way to Get 512GB of VRAM for Local AI → https://youtu.be/6tihP50zlcE
Every Way to Get 32GB VRAM for Local AI → https://youtu.be/uavLRbbfM94
Is 16GB All You Need for Serious Local LLM? → https://youtu.be/i9mRnbdDYxk
What It Actually Costs to Run DeepSeek V4.1 Flash Locally → https://youtu.be/04ePeOyRN0k
Run 125B Models 6× Faster Than llama.cpp (Strata) → https://youtu.be/m0VHx73SAG0
Why Your Mac Is Slow at Local AI (And It's Not the Chip) → https://youtu.be/x_qmqqp6d7s
Can a Mac Mini Replace Your $200 AI Subscription? → https://youtu.be/3R7yba577uA

📊 Sources
Artificial Analysis Intelligence Index v5: https://artificialanalysis.ai/leaderboards/models
Unsloth quant sizes: https://unsloth.ai/docs/models/kimi-k3
OpenRouter model pricing: https://openrouter.ai
Prices and specs as of late October / November 1, 2026. Kimi K3.1 Pro's weights are promised, not yet released, and no one has scored the 2-bit build.

#LocalAI #LocalLLM #MacStudio