"What model should I run on my 16GB Mac?" or "Will DeepSeek R1 fit on my 4060?"
I got tired of giving the same answer to my friends, so I analyse 125+ models across every hardware tier and turned it into a free tool. But first, here's the cheat sheet.
The cheat sheet (February 2026)
8GB: Qwen 3 8B (general), Qwen 2.5 Coder 7B (code), DeepSeek R1 8B (reasoning)
16GB: GLM 4.5 Air 14B or Qwen 3 14B (best balance depends on task)
32GB: Qwen 3 32B (general), Qwen 2.5 Coder 32B (coding)
64GB+: Llama 3.3 70B (+ GLM 4.5 Air variants depending on context/speed needs)
No universal best model — best choice depends on quantization, context size, and workload.
All recommendations use Q4_K_M quantization unless noted. Available on LM Studio.
What actually matters (not benchmarks)
Benchmarks don't tell you if a model will run smooth on your machine. Here's what does:
File size vs available RAM. If the model + OS overhead exceeds your RAM, it swaps to disk and becomes unusable. A 9GB model on a 8GB machine = pain.
Quantization choice. Q4_K_M is the sweet spot for most setups. Q8 only if you have RAM headroom. Q2/Q3 loses too much quality to be worth it.
VRAM fit on NVIDIA. If the entire model fits in your GPU VRAM, you get 3-5x speed boost over CPU inference. A 9GB model on a 12GB GPU = full GPU acceleration. Same model on a 8GB GPU = partial offload, much slower.
Context window overhead. Everyone forgets this. 16K context adds ~1-2GB of KV cache on top of the model file. That "fits in RAM" model suddenly doesn't when you have a long conversation.
Chinese models are dominating local inference in 2026
The biggest shift this year: Qwen, DeepSeek, and GLM are beating Western models at almost every size tier for local use.
Qwen 3 14B on 16GB RAM gives you quality that rivals GPT-4 for most daily tasks. DeepSeek R1 series is the reasoning king at every size. GLM 4.5 Air is the new speed champion in the 14B class, outperforming models twice its size.
GLM-5 just dropped (744B params, open-weight MIT license) but right now it's a datacenter model — you need 8x H100s to run it. The community is already asking for Air and Flash distilled versions. When those land, expect them to shake up the 16-32GB tier.
The tool
I built LocalClaw to automate all of this.
You answer 3-4 questions about your hardware (OS, RAM, GPU VRAM, use case) and it tells you exactly which model + quantization to download for LM Studio.
It also explains WHY it picked that model — RAM usage percentage, VRAM fit, context window overhead. Not just "download this" but "here's why this is the right choice for your specific setup."
125+ models. macOS, Windows, Linux. Works with NVIDIA GPUs and Apple Silicon.
No account, no data collected, runs entirely in your browser.
Why LM Studio specifically?
I get asked this a lot. LM Studio vs Ollama vs llama.cpp | why focus on one?
LM Studio gives you a chat UI + an OpenAI-compatible API server + model management in one app. For someone who just wants to run a local LLM and start chatting, it's the lowest friction path. You download it, search for a model, click download, click load, and you're talking to a local AI.
Ollama is great for developers who want CLI-first workflows. llama.cpp is great for people who want maximum control.
But most people asking "what model should I run?" just want the simplest path to a working local AI. That's LM Studio.
LocalClaw recommends for LM Studio but the model recommendations apply to any runtime — the right model for your hardware is the right model regardless of what loads it.
What's next
I'm working on adding real benchmark data (tokens/sec) per hardware config, and a community section where people can submit their actual performance numbers. If you run a model and want to share your tokens/sec + hardware specs, I'd love to include it.
Feedback welcome — especially if you think a recommendation is wrong for your hardware. That's how the tool gets better.
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