Check my GPU

on

Can the Apple M2 Pro (32 GB) run SQLCoder 7B v2?

62 ptsGood (B) · 62 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.7/15

Yes, it runs well

Runs well. At Q3_K_M it needs about 9.9 GB of 21.4 GB usable, with an estimated speed of 40.5 tok/s.

Best quant
Q3_K_M
Generation speed
40.5 tok/s
Prompt processing
626 tok/s
First token (1K prompt)
1.6 s
Largest safe context
16K

Download SQLCoder 7B v2

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/defog/sqlcoder-7b-2 (opens in a new tab)

ollama run hf.co/defog/sqlcoder-7b-2

Original weights · Hugging Face

Full-precision files from Defog, for fine-tuning, vLLM, Transformers.

huggingface.co/defog/sqlcoder-7b-2 (opens in a new tab)

No sign-in needed.

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.11.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.7 / 15
Total62.0 / 10062 ptsGood (B) · 62 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.7/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 9.3 GB 21.4 GB 51.5 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15 GGUF files (opens in a new tab)
Q3_K_M 9.9 GB 21.4 GB 40.5 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.7/15 GGUF files (opens in a new tab)
Q4_K_M 10.7 GB 21.4 GB 31.4 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 13.3/15, Memory use 11.5/15 GGUF files (opens in a new tab)
Q5_K_M 11.4 GB 21.4 GB 25.9 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 12.1/15, Memory use 12.3/15 GGUF (opens in a new tab)
Q6_K 12.1 GB 21.4 GB 21.8 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 13.1/15 GGUF files (opens in a new tab)
Q8_0 13.8 GB 21.4 GB 16.0 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 9.5/15, Memory use 14.8/15 GGUF files (opens in a new tab)
F16 20.1 GB 21.4 GB 7.4 tok/s Tight fit 58 ptsGood (B) · 58 of 100 points: Model quality 13.5/45, Memory fit 24.0/25, Speed 6.4/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.