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Can the AMD Ryzen AI Max+ 395 (128 GB) run Qwen3 Reranker 8B?

89Excellent quality (S) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q6_K it needs about 15.3 GB of 96.0 GB usable and makes one decision in about 161 ms.

Best quant
Q6_K
Time per decision
161 ms
Decisions per second
6.2/s
Input assumed
512 tokens
Returns
Probability or score

Best app to run it on the AMD Ryzen AI Max+ 395 (128 GB)

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull mradermacher/Qwen3-Reranker-8B-GGUF:Q6_K
  • llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
  • vLLM Serves the scoring head directly (rerank, classify or reward) with batching.

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull mradermacher/Qwen3-Reranker-8B-GGUF:Q6_K
  • llama.cpp Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
  • vLLM Serves the scoring head directly (rerank, classify or reward) with batching.

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 Qwen3 Reranker 8B

Recommended for the AMD Ryzen AI Max+ 395 (128 GB): Q6_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in llama.cpp (llama-server --rerank), Lemonade and other llama.cpp servers with a rerank endpoint. No sign-in needed.

Download the Q6_K file (opens in a new tab)

huggingface.co/mradermacher/Qwen3-Reranker-8B-GGUF (opens in a new tab)

llama-server -hf mradermacher/Qwen3-Reranker-8B-GGUF:Q6_K --rerank

Original weights · Hugging Face

Full-precision files from Alibaba, for Transformers, Sentence Transformers, Text Embeddings Inference, Infinity or vLLM.

huggingface.co/Qwen/Qwen3-Reranker-8B (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 89Excellent quality (S) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q6_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: S 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs well
How well it runs on the AMD Ryzen AI Max+ 395 (128 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.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.40.1 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.11.8 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.15.0 / 15
Match score91.9 / 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 Max+ 395 (128 GB)

Quant Memory needed Available Time per decision Runs Quality Download
Q4_K_M 13.6 GB 96.0 GB 148 ms Runs well 85Excellent quality (S) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 14.4 GB 96.0 GB 154 ms Runs well 87Excellent quality (S) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 15.3 GB 96.0 GB 161 ms Runs well 89Excellent quality (S) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 17.3 GB 96.0 GB 177 ms Runs well 90Excellent quality (S) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 25.0 GB 96.0 GB 247 ms Runs well 90Excellent quality (S) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 512-token input and about 1 tokens written. Batching servers process many requests at once.

Other hardware that runs it

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.