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Can the NVIDIA GeForce GTX 1660 Ti run Qwen3.5 9B?

82 ptsGreat (A) · 82 of 100 points: Model quality 28.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q3_K_M it needs about 5.9 GB of 6.0 GB usable, with an estimated speed of 47.4 tok/s.

Best quant
Q3_K_M
Generation speed
47.4 tok/s
Prompt processing
403 tok/s
First token (1K prompt)
2.5 s
Largest safe context
11K

Download Qwen3.5 9B

Recommended for the NVIDIA GeForce GTX 1660 Ti: Q3_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run qwen3.5:9b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

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

Download the Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/Qwen3.5-9B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.5-9B-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.5-9B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/QwenLM/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

How the A 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.28.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total82.7 / 10082 ptsGreat (A) · 82 of 100 points: Model quality 28.7/45, Memory fit 24.0/25, 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 GTX 1660 Ti

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.0 GB 6.0 GB 60.2 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 5.9 GB 6.0 GB 47.4 tok/s Tight fit 82 ptsGreat (A) · 82 of 100 points: Model quality 28.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 7.1 GB 6.0 GB 23.0 tok/s Runs with offload 68 ptsGood (B) · 68 of 100 points: Model quality 32.5/45, Memory fit 10.0/25, Speed 11.4/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 8.0 GB 6.0 GB 15.3 tok/s Runs with offload 67 ptsGood (B) · 67 of 100 points: Model quality 33.2/45, Memory fit 10.0/25, Speed 9.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 9.1 GB 6.0 GB 11.1 tok/s Heavy offload 60 ptsGood (B) · 60 of 100 points: Model quality 33.9/45, Memory fit 4.0/25, Speed 7.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 11.4 GB 6.0 GB 6.8 tok/s Heavy offload 59 ptsGood (B) · 59 of 100 points: Model quality 34.2/45, Memory fit 4.0/25, Speed 6.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 20.5 GB 6.0 GB 2.5 tok/s Heavy offload 56 ptsUsable (C) · 56 of 100 points: Model quality 34.2/45, Memory fit 4.0/25, Speed 3.7/15, Memory use 15.0/15 GGUF (opens in a new tab) 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.