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Can the NVIDIA GeForce RTX 4070 SUPER run Baichuan 2 7B Chat?

63 ptsGood (B) · 63 of 100 points: Model quality 8.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 9.5 GB of 12.0 GB usable, with an estimated speed of 57.2 tok/s.

Best quant
Q6_K
Generation speed
57.2 tok/s
Prompt processing
3,403 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Download Baichuan 2 7B Chat

Recommended for the NVIDIA GeForce RTX 4070 SUPER: Q6_K. 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 Q6_K file (opens in a new tab)

huggingface.co/mradermacher/Baichuan2-7B-Chat-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Baichuan2-7B-Chat-i1-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/baichuan-inc/Baichuan2-7B-Chat (opens in a new tab)

No sign-in needed.

Links checked 7 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.8.9 / 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
Total63.9 / 10063 ptsGood (B) · 63 of 100 points: Model quality 8.9/45, 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 4070 SUPER

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.4 GB 12.0 GB 135 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 6.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.2/15 GGUF (opens in a new tab)
Q3_K_M 7.0 GB 12.0 GB 107 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 7.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.5/15 GGUF (opens in a new tab)
Q4_K_M 7.9 GB 12.0 GB 82.4 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 8.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 8.7 GB 12.0 GB 68.1 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 8.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 9.5 GB 12.0 GB 57.2 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 8.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 11.3 GB 12.0 GB 41.9 tok/s Tight fit 63 ptsGood (B) · 63 of 100 points: Model quality 9.0/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 18.4 GB 12.0 GB 5.1 tok/s Heavy offload 33 ptsPoor (D) · 33 of 100 points: Model quality 9.0/45, Memory fit 4.0/25, Speed 5.4/15, Memory use 15.0/15 Original weights (opens in a new tab)

Memory figures assume 4K 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.