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Can the Intel Arc A580 run Qwen3 Reranker 8B?

85Excellent quality (S) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 6.3 GB of 8.0 GB usable and makes one decision in about 331 ms.

Best quant
Q4_K_M
Time per decision
331 ms
Decisions per second
3.0/s
Input assumed
512 tokens
Returns
Probability or score

Best app to run it on the Intel Arc A580

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:Q4_K_M --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.

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:Q4_K_M --rerank
  • vLLM Serves the scoring head directly (rerank, classify or reward) with batching.
  • Lemonade Serves the GGUF build behind a /v1/rerank endpoint with llama-server --rerank.

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 Intel Arc A580: Q4_K_M. 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 Q4_K_M 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:Q4_K_M --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 85Excellent quality (S) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 86/100 (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: S 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs well
How well it runs on the Intel Arc A580: 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.38.5 / 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.8.2 / 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 score86.7 / 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 Intel Arc A580

Quant Memory needed Available Time per decision Runs Quality Download
Q4_K_M 6.3 GB 8.0 GB 331 ms Runs well 85Excellent quality (S) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 7.1 GB 8.0 GB 335 ms Runs welltight fit 87Excellent quality (S) · 87 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 8.0 GB 8.0 GB 339 ms Runs welloffloads to RAM 89Excellent quality (S) · 89 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 10.0 GB 8.0 GB 444 ms Slowoffloads to RAM 90Excellent quality (S) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 17.7 GB 8.0 GB 815 ms Slowheavy RAM offload 90Excellent quality (S) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 64/100 (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.