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Can the AMD Ryzen AI 9 HX PRO 370 / 375 NAS (96 GB) run Qwen3.8 Flash Next (125B-A6B)?

67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (over GPU memory limit)

Runs well (over GPU memory limit). At Q2_K it needs about 78.6 GB of 72.0 GB usable, with an estimated speed of 24.3 tok/s.

Best quant
Q2_K
Generation speed
24.3 tok/s
Prompt processing
2,975 tok/s
First token (1K prompt)
0.3 s
Largest safe context
—

Best app to run it on the AMD Ryzen AI 9 HX PRO 370 / 375 NAS (96 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Usually the first to support new model architectures.
llama-server -hf unsloth/Qwen3.8-Flash-Next-GGUF:UD-Q2_K_XL
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • KoboldCpp Splits the model between GPU memory and system RAM, which this setup needs.

Want an agent? Qwen3.8 Flash Next (125B-A6B) supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Qwen3.8 Flash Next (125B-A6B)

Recommended for the AMD Ryzen AI 9 HX PRO 370 / 375 NAS (96 GB): Q2_K. 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 Q2_K (UD-Q2_K_XL) files (opens in a new tab)

huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.8-Flash-Next-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.8-Flash-Next (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)

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

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

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

How it’s rated

Quality 67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 wellover GPU memory limit
How well it runs on the AMD Ryzen AI 9 HX PRO 370 / 375 NAS (96 GB): 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.30.4 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.11.7 / 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 score67.1 / 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 AMD Ryzen AI 9 HX PRO 370 / 375 NAS (96 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 78.6 GB 72.0 GB 24.3 tok/s Runs wellover GPU memory limit 67Good quality (B) · 67 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q2_K_XL) · folder (opens in a new tab)
Q3_K_M 94.8 GB 72.0 GB — Won't run 81Great quality (A) · 81 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q3_K_XL) · folder (opens in a new tab)
Q4_K_M 116 GB 72.0 GB — Won't run 91Excellent quality (A+) · 91 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q5_K_M 134 GB 72.0 GB — Won't run 93Excellent quality (A+) · 93 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q5_K_XL) · folder (opens in a new tab)
Q6_K 154 GB 72.0 GB — Won't run 95Excellent quality (A+) · 95 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q6_K_XL) · folder (opens in a new tab)
Q8_0 197 GB 72.0 GB — Won't run 96Excellent quality (A+) · 96 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 367 GB 72.0 GB — Won't run 96Excellent quality (A+) · 96 of 100: how capable the model is at F16, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (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 Qwen3.8 Flash Next (125B-A6B) need on the AMD Ryzen AI 9 HX PRO 370 / 375 NAS 96GB?

At Q2_K it needs about 78.6 GB. The AMD Ryzen AI 9 HX PRO 370 / 375 NAS 96GB has 72.0 GB usable.

How fast is Qwen3.8 Flash Next (125B-A6B) on the AMD Ryzen AI 9 HX PRO 370 / 375 NAS 96GB?

About 24.3 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen3.8 Flash Next (125B-A6B) should I use on the AMD Ryzen AI 9 HX PRO 370 / 375 NAS 96GB?

Q2_K. 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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