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Can the NVIDIA RTX 4000 Ada Generation Laptop GPU run Qwen1.5 MoE A2.7B Chat?

22Basic quality (D) · 22 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q4_K_M it needs about 11.5 GB of 12.0 GB usable, with an estimated speed of 118 tok/s.

Best quant
Q4_K_M
Generation speed
118 tok/s
Prompt processing
8,960 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

Best app to run it on the NVIDIA RTX 4000 Ada Generation Laptop GPU

On Windows

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/Qwen/Qwen1.5-MoE-A2.7B-Chat

On Linux

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/Qwen/Qwen1.5-MoE-A2.7B-Chat

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 Qwen1.5 MoE A2.7B Chat

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-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 22Basic quality (D) · 22 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (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 welltight fit
How well it runs on the NVIDIA RTX 4000 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.10.3 / 45
Memory fitFull marks when everything sits in GPU 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
Match score64.3 / 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 RTX 4000 Ada Generation Laptop GPU

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.5 GB 12.0 GB 161 tok/s Runs great 16Basic quality (D) · 16 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 9.8 GB 12.0 GB 139 tok/s Runs great 20Basic quality (D) · 20 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. All files (opens in a new tab)
Q4_K_M 11.5 GB 12.0 GB 118 tok/s Runs welltight fit 22Basic quality (D) · 22 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 13.0 GB 12.0 GB 77.5 tok/s Runs welloffloads to RAM 23Basic quality (D) · 23 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 14.6 GB 12.0 GB 50.8 tok/s Runs welloffloads to RAM 23Basic quality (D) · 23 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. All files (opens in a new tab)
Q8_0 18.0 GB 12.0 GB 28.2 tok/s Slowheavy RAM offload 24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 31.5 GB 12.0 GB 9.4 tok/s Slowheavy RAM offload 24Basic quality (D) · 24 of 100: how capable the model is at F16, whatever it runs on. Match score 37/100 (quality, fit, speed and memory use) sets the ranking. 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 Qwen1.5 MoE A2.7B Chat need on the NVIDIA RTX 4000 Ada Generation Laptop GPU 12GB?

At Q4_K_M it needs about 11.5 GB. The NVIDIA RTX 4000 Ada Generation Laptop GPU 12GB has 12.0 GB usable.

How fast is Qwen1.5 MoE A2.7B Chat on the NVIDIA RTX 4000 Ada Generation Laptop GPU 12GB?

About 118 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen1.5 MoE A2.7B Chat should I use on the NVIDIA RTX 4000 Ada Generation Laptop GPU 12GB?

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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