Skywork Reward V2 Qwen3 8B on Apple M5 Max (64 GB)

Can the Apple M5 Max (64 GB) run Skywork Reward V2 Qwen3 8B?

Yes, but slowly

Slow. At Q8_0 it needs about 10.5 GB of 48.0 GB usable and makes one decision in about 723 ms.

Best precision: Q8_0
Time per decision: 723 ms
Decisions per second: 1.4/s
Input assumed: 1,024 tokens
Returns: Probability or score

Best app to run it on the Apple M5 Max (64 GB)

On macOS

Sentence Transformers

Python library · runs on PyTorch (Transformers)

A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

Get Sentence Transformers

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 Skywork Reward V2 Qwen3 8B

Original weights · Hugging Face

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

huggingface.co/Skywork/Skywork-Reward-V2-Qwen3-8B (opens in a new tab)

No sign-in needed.

Links checked 5 hours ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q8_0, 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 Slow: How well it runs on the Apple M5 Max (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.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.	37.8 / 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.	5.6 / 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 score	83.4 / 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 Apple M5 Max (64 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	10.5 GB	48.0 GB	723 ms	Slow	84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	17.6 GB	48.0 GB	831 ms	Slow	84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Times are for one request with a 1,024-token input. Batching servers process many requests at once.

Other hardware that runs it

84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Q8_0 · 98 ms · Runs great
84Great quality (A) · 84 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. AMD Radeon RX 7900 XTX Q8_0 · 115 ms · Runs great
84Great quality (A) · 84 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. NVIDIA GeForce RTX 5090 Q8_0 · 116 ms · Runs great
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 144 ms · Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Q8_0 · 146 ms · Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Q8_0 · 212 ms · Runs well

Quick answers

How much memory does Skywork Reward V2 Qwen3 8B need on the M5 Max 64GB?

At Q8_0 it needs about 10.5 GB. The M5 Max 64GB has 48.0 GB usable.

How fast is Skywork Reward V2 Qwen3 8B on the M5 Max 64GB?

It makes one decision in about 723 ms at Q8_0, 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.