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Can the NVIDIA GeForce RTX 4060 run Qwen2.5 14B Instruct 1M?

50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At Q3_K_M it needs about 9.8 GB of 8.0 GB usable, with an estimated speed of 16.4 tok/s.

Best quant
Q3_K_M
Generation speed
16.4 tok/s
Prompt processing
605 tok/s
First token (1K prompt)
1.7 s
Largest safe context
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Best app to run it on the NVIDIA GeForce RTX 4060

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 1M in LM Studio

  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama 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 1M in LM Studio

  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
  • Ollama 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 1M

Recommended for the NVIDIA GeForce RTX 4060: Q3_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 Q3_K_M file (opens in a new tab)

huggingface.co/bartowski/Qwen2.5-14B-Instruct-1M-GGUF (opens in a new tab)

ollama run hf.co/bartowski/Qwen2.5-14B-Instruct-1M-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-14B-Instruct-1M (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 welloffloads to RAM
How well it runs on the NVIDIA GeForce RTX 4060: 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.22.7 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.9.6 / 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 score57.3 / 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 4060

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.5 GB 8.0 GB 29.2 tok/s Runs welloffloads to RAM 42Fair quality (C) · 42 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 9.8 GB 8.0 GB 16.4 tok/s Runs welloffloads to RAM 50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 11.6 GB 8.0 GB 10.1 tok/s Slowheavy RAM offload 57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 13.1 GB 8.0 GB 7.5 tok/s Slowheavy RAM offload 58Good quality (B) · 58 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 (opens in a new tab)
Q6_K 14.7 GB 8.0 GB 5.8 tok/s Slowheavy RAM offload 59Good quality (B) · 59 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 (opens in a new tab)
Q8_0 18.3 GB 8.0 GB 3.8 tok/s Slowheavy RAM offload 60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 32.2 GB 8.0 GB — Won't run 60Good quality (B) · 60 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 (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 1M need on the RTX 4060 8GB?

At Q3_K_M it needs about 9.8 GB. The RTX 4060 8GB has 8.0 GB usable.

How fast is Qwen2.5 14B Instruct 1M on the RTX 4060 8GB?

About 16.4 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen2.5 14B Instruct 1M should I use on the RTX 4060 8GB?

Q3_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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