Qwen3.5 4B on NVIDIA GeForce GTX 1660 Ti
Can the NVIDIA GeForce GTX 1660 Ti run Qwen3.5 4B?
Yes, it runs well
Runs well. At Q6_K it needs about 5.0 GB of 6.0 GB usable, with an estimated speed of 52.6 tok/s.
- Best quant
Q6_K- Generation speed
- 52.6 tok/s
- Prompt processing
- 834 tok/s
- First token (1K prompt)
- 1.2 s
- Largest safe context
- 38K
Download Qwen3.5 4B
Recommended for the NVIDIA GeForce GTX 1660 Ti: Q6_K. 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 qwen3.5:4bQuantized 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/unsloth/Qwen3.5-4B-GGUF (opens in a new tab)
ollama run hf.co/unsloth/Qwen3.5-4B-GGUF:Q6_KOriginal weights · Hugging Face
Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.
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/Qwen3.8How the A grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 27.6 / 45 | |
|---|---|---|
| Memory fit | 25.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 15.0 / 15 | |
| Total | 82.6 / 100 | 82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 |
Every quantization on the NVIDIA GeForce GTX 1660 Ti
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 3.0 GB | 6.0 GB | 125 tok/s | Runs well | 71 ptsGood (B) · 71 of 100 points: Model quality 19.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.7/15 | GGUF (UD-Q2_K_XL) (opens in a new tab) |
Q3_K_M | 3.5 GB | 6.0 GB | 98.1 tok/s | Runs well | 76 ptsGreat (A) · 76 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.3/15 | GGUF (opens in a new tab) |
Q4_K_M | 4.0 GB | 6.0 GB | 75.9 tok/s | Runs well | 81 ptsGreat (A) · 81 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q5_K_M | 4.5 GB | 6.0 GB | 62.7 tok/s | Runs well | 82 ptsGreat (A) · 82 of 100 points: Model quality 27.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q6_K | 5.0 GB | 6.0 GB | 52.6 tok/s | Runs well | 82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
Q8_0 | 6.1 GB | 6.0 GB | 36.0 tok/s | Runs with offload | 67 ptsGood (B) · 67 of 100 points: Model quality 27.9/45, Memory fit 10.0/25, Speed 14.2/15, Memory use 15.0/15 | GGUF (opens in a new tab) |
F16 | 10.5 GB | 6.0 GB | 6.8 tok/s | Heavy offload | 53 ptsUsable (C) · 53 of 100 points: Model quality 27.9/45, Memory fit 4.0/25, Speed 6.2/15, 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
- 82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER
- 82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER
- 82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti
- 82 ptsGreat (A) · 82 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080
- 82 ptsGreat (A) · 82 of 100 points: Model quality 27.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.7/15 NVIDIA GeForce RTX 4060
- 80 ptsGreat (A) · 80 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.3/15 Intel Arc B580
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.