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Can the NVIDIA GeForce RTX 4070 Ti SUPER run Baichuan 2 13B Chat?

24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 13.2 GB of 16.0 GB usable, with an estimated speed of 59.3 tok/s.

Best quant
Q4_K_M
Generation speed
59.3 tok/s
Prompt processing
2,284 tok/s
First token (1K prompt)
0.4 s
Largest safe context
4K

Best app to run it on the NVIDIA GeForce RTX 4070 Ti SUPER

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Baichuan 2 13B Chat in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Baichuan 2 13B Chat in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Baichuan 2 13B Chat

Recommended for the NVIDIA GeForce RTX 4070 Ti SUPER: Q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/mradermacher/Baichuan2-13B-Chat-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Baichuan2-13B-Chat-i1-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/baichuan-inc/Baichuan2-13B-Chat (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the NVIDIA GeForce RTX 4070 Ti SUPER: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.11.1 / 45
Memory fitFull marks when everything sits in GPU 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
Match score66.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA GeForce RTX 4070 Ti SUPER

Quant Memory needed Available Speed Runs Quality Download
Q2_K 10.3 GB 16.0 GB 97.5 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 11.6 GB 16.0 GB 76.7 tok/s Runs great 21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 13.2 GB 16.0 GB 59.3 tok/s Runs great 24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 14.6 GB 16.0 GB 49.0 tok/s Runs welltight fit 25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 16.2 GB 16.0 GB 36.7 tok/s Runs welloffloads to RAM 25Basic quality (D) · 25 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 19.5 GB 16.0 GB 9.6 tok/s Slowoffloads to RAM 26Basic quality (D) · 26 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 32.6 GB 16.0 GB 2.2 tok/s Very slowheavy RAM offload 26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 34/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Baichuan 2 13B Chat need on the RTX 4070 Ti SUPER 16GB?

At Q4_K_M it needs about 13.2 GB. The RTX 4070 Ti SUPER 16GB has 16.0 GB usable.

How fast is Baichuan 2 13B Chat on the RTX 4070 Ti SUPER 16GB?

About 59.3 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Baichuan 2 13B Chat should I use on the RTX 4070 Ti SUPER 16GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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