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Can the Apple M5 Ultra (512 GB) run Zamba2 7B Instruct?

38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 46.8 GB of 384 GB usable, with an estimated speed of 40.4 tok/s.

Best quant
F16
Generation speed
40.4 tok/s
Prompt processing
3,176 tok/s
First token (1K prompt)
0.3 s
Largest safe context
4K

Best app to run it on the Apple M5 Ultra (512 GB)

On macOS

Docker Model Runner

Command line + local API · runs on llama.cpp, vLLM

  • Handy if you already work in Docker.
docker model run hf.co/Zyphra/Zamba2-7B-Instruct

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 Zamba2 7B Instruct

Original weights · Hugging Face

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

huggingface.co/Zyphra/Zamba2-7B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at F16, 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 Apple M5 Ultra (512 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.17.1 / 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
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.2.8 / 15
Match score59.9 / 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 Apple M5 Ultra (512 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 35.0 GB 384 GB 283 tok/s Runs great 26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 35.7 GB 384 GB 223 tok/s Runs great 31Basic quality (D) · 31 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 36.6 GB 384 GB 172 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 37.3 GB 384 GB 142 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 38.1 GB 384 GB 120 tok/s Runs great 37Basic quality (D) · 37 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 39.9 GB 384 GB 87.7 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 46.8 GB 384 GB 40.4 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Zamba2 7B Instruct need on the M5 Ultra 512GB?

At F16 it needs about 46.8 GB. The M5 Ultra 512GB has 384 GB usable.

How fast is Zamba2 7B Instruct on the M5 Ultra 512GB?

About 40.4 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Zamba2 7B Instruct should I use on the M5 Ultra 512GB?

F16. 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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