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Can the NVIDIA RTX 1000 Ada Generation Laptop GPU run Qwen2.5 14B Instruct?

58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (heavy RAM offload)

Slow (heavy RAM offload). At Q4_K_M it needs about 11.5 GB of 6.0 GB usable, with an estimated speed of 7.5 tok/s.

Best quant
Q4_K_M
Generation speed
7.5 tok/s
Prompt processing
339 tok/s
First token (1K prompt)
3.0 s
Largest safe context
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Best app to run it on the NVIDIA RTX 1000 Ada Generation Laptop GPU

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open Qwen2.5 14B Instruct in LM Studio

  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open Qwen2.5 14B Instruct in LM Studio

  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.

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 Qwen2.5 14B Instruct

Recommended for the NVIDIA RTX 1000 Ada Generation Laptop GPU: Q4_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.

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

ollama run qwen2.5:14b-instruct-q4_K_M

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/Qwen/Qwen2.5-14B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen2.5-14B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-14B-Instruct (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 (opens in a new tab)

Qwen’s main repo, renamed from Qwen2.5 to Qwen3; it still documents Qwen2.5.

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

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 51/100 (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 Slowheavy RAM offload
How well it runs on the NVIDIA RTX 1000 Ada Generation Laptop GPU: 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.26.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.6.5 / 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 score52.0 / 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 RTX 1000 Ada Generation Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.4 GB 6.0 GB 14.9 tok/s Slowheavy RAM offload 43Fair quality (C) · 43 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (opens in a new tab)
Q3_K_M 9.7 GB 6.0 GB 10.5 tok/s Slowheavy RAM offload 52Fair quality (C) · 52 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 2 (opens in a new tab)
Q4_K_M 11.5 GB 6.0 GB 7.5 tok/s Slowheavy RAM offload 58Good quality (B) · 58 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 3 (opens in a new tab)
Q5_K_M 12.9 GB 6.0 GB 5.9 tok/s Slowheavy RAM offload 60Good quality (B) · 60 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 3 (opens in a new tab)
Q6_K 14.6 GB 6.0 GB 4.8 tok/s Slowheavy RAM offload 61Good quality (B) · 61 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 4 (opens in a new tab)
Q8_0 18.1 GB 6.0 GB — Won't run 62Good quality (B) · 62 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 4 (opens in a new tab)
F16 31.9 GB 6.0 GB — Won't run 62Good quality (B) · 62 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 8 (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

Quick answers

How much memory does Qwen2.5 14B Instruct need on the NVIDIA RTX 1000 Ada Generation Laptop GPU 6GB?

At Q4_K_M it needs about 11.5 GB. The NVIDIA RTX 1000 Ada Generation Laptop GPU 6GB has 6.0 GB usable.

How fast is Qwen2.5 14B Instruct on the NVIDIA RTX 1000 Ada Generation Laptop GPU 6GB?

About 7.5 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen2.5 14B Instruct should I use on the NVIDIA RTX 1000 Ada Generation Laptop GPU 6GB?

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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