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Can the NVIDIA GeForce RTX 4060 Ti 16GB run SQLCoder 7B v2?

68 ptsGood (B) · 68 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 10.5 GB of 16.0 GB usable, with an estimated speed of 43.3 tok/s.

Best quant
Q5_K_M
Generation speed
43.3 tok/s
Prompt processing
2,361 tok/s
First token (1K prompt)
0.4 s
Largest safe context
16K

Download SQLCoder 7B v2

Recommended for the NVIDIA GeForce RTX 4060 Ti 16GB: Q5_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q5_K_M file (opens in a new tab)

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

ollama run hf.co/defog/sqlcoder-7b-2:Q5_K_M

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 6 days 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.13.1 / 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.15.0 / 15
Total68.1 / 10068 ptsGood (B) · 68 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

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

Every quantization on the NVIDIA GeForce RTX 4060 Ti 16GB

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 8.4 GB 16.0 GB 86.1 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 9.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.1/15 GGUF files (opens in a new tab)
Q3_K_M 9.0 GB 16.0 GB 67.8 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.0/15 GGUF files (opens in a new tab)
Q4_K_M 9.8 GB 16.0 GB 52.5 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 12.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.1/15 GGUF files (opens in a new tab)
Q5_K_M 10.5 GB 16.0 GB 43.3 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 13.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 11.2 GB 16.0 GB 36.4 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 13.4/45, Memory fit 25.0/25, Speed 14.3/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 12.8 GB 16.0 GB 26.7 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 13.5/45, Memory fit 25.0/25, Speed 12.3/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 19.2 GB 16.0 GB 6.9 tok/s Runs with offload 44 ptsUsable (C) · 44 of 100 points: Model quality 13.5/45, Memory fit 10.0/25, Speed 6.2/15, 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.