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Can the AMD Radeon RX 7900M run Qwen3 Reranker 8B?

89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q6_K it needs about 8.4 GB of 16.0 GB usable and makes one decision in about 111 ms.

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

Best app to run it on the AMD Radeon RX 7900M

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
llama-server -hf mradermacher/Qwen3-Reranker-8B-GGUF:Q6_K --rerank
  • Lemonade Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
  • Sentence Transformers A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.
llama-server -hf mradermacher/Qwen3-Reranker-8B-GGUF:Q6_K --rerank
  • Lemonade 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 Radeon RX 7900M: 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 (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 94/100 (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: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the AMD Radeon RX 7900M: 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 everything sits in GPU memory; fewer when part spills to system RAM.25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.14.2 / 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 score94.3 / 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 AMD Radeon RX 7900M

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 6.7 GB 16.0 GB 105 ms Runs great 85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 7.5 GB 16.0 GB 108 ms Runs great 87Excellent quality (A+) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 8.4 GB 16.0 GB 111 ms Runs great 89Excellent quality (A+) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 10.4 GB 16.0 GB 118 ms Runs great 90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 18.1 GB 16.0 GB 205 ms Runs welloffloads to RAM 90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 75/100 (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

Quick answers

How much memory does Qwen3 Reranker 8B need on the RX 7900M 16GB?

At Q6_K it needs about 8.4 GB. The RX 7900M 16GB has 16.0 GB usable.

How fast is Qwen3 Reranker 8B on the RX 7900M 16GB?

It makes one decision in about 111 ms at Q6_K, an estimate from the hardware and the size of the model.

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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