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Can the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (64 GB) run Qwen3 Coder Next (80B-A3B)?

60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (far over GPU memory limit)

Slow (far over GPU memory limit). At Q2_K it needs about 35.2 GB of 32.0 GB usable, with an estimated speed of 47.0 tok/s.

Best quant
Q2_K
Generation speed
47.0 tok/s
Prompt processing
4,427 tok/s
First token (1K prompt)
0.3 s
Largest safe context
—

Best app to run it on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (64 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • One command with Ollama’s own build of this model.
  • Serves an OpenAI-compatible API that other apps and coding tools can use.
ollama run qwen3-coder-next
  • llama.cpp 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 Coder Next (80B-A3B) 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 Coder Next (80B-A3B)

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 qwen3-coder-next

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/Qwen/Qwen3-Coder-Next-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen3-Coder-Next-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-Coder-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-Coder (opens in a new tab)

Qwen’s coder repo, renamed from Qwen2.5-Coder to Qwen3-Coder.

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

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

How it’s rated

Quality 60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (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 Slowfar over GPU memory limit
How well it runs on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (64 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.27.1 / 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.4.0 / 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 score61.1 / 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 7 8845HS / Ryzen 7 255 NAS (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 35.2 GB 32.0 GB 47.0 tok/s Slowfar over GPU memory limit 60Good quality (B) · 60 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 42.4 GB 32.0 GB — Won't run 72Great quality (A) · 72 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 52.0 GB 32.0 GB — Won't run 81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q5_K_M 59.9 GB 32.0 GB — Won't run 83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q6_K 68.7 GB 32.0 GB — Won't run 85Excellent quality (A+) · 85 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
Q8_0 87.8 GB 32.0 GB — Won't run 86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab)
F16 163 GB 32.0 GB — Won't run 86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (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 Coder Next (80B-A3B) need on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 64GB?

At Q2_K it needs about 35.2 GB. The AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 64GB has 32.0 GB usable.

How fast is Qwen3 Coder Next (80B-A3B) on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 64GB?

About 47.0 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen3 Coder Next (80B-A3B) should I use on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 64GB?

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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