Can the Apple M5 Ultra (256 GB) run GLiDE 27B?

Yes, it runs well

Runs well. At Q8_0 it needs about 34.8 GB of 192 GB usable and makes one decision in about 315 ms.

Best precision: Q8_0
Time per decision: 315 ms
Decisions per second: 3.2/s
Input assumed: 256 tokens
Returns: Probability or score

Best app to run it on the Apple M5 Ultra (256 GB)

On macOS

Python library · runs on PyTorch (Transformers)

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

python -c "from transformers import pipeline; print(pipeline('text-classification', model='fastino/GLiDE-27B')('text'))"

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 GLiDE 27B

Original weights · Hugging Face

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

huggingface.co/fastino/GLiDE-27B (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 87/100 (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 Runs well: How well it runs on the Apple M5 Ultra (256 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.	38.7 / 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.	8.5 / 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	87.2 / 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 (256 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q8_0`	34.8 GB	192 GB	315 ms	Runs well	86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	59.3 GB	192 GB	361 ms	Runs well	86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Times are for one request with a 256-token input. Batching servers process many requests at once.

Other hardware that runs it

86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell F16 · 97 ms · Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. AMD Ryzen AI Max+ 395 (128 GB) Q8_0 · 201 ms · Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB10 (128 GB) Q8_0 · 329 ms · Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX Spark N1X (6144-core GPU) (128 GB) Q8_0 · 354 ms · Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI300X F16 · 86 ms · Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325X F16 · 86 ms · Runs great

Quick answers

How much memory does GLiDE 27B need on the M5 Ultra 256GB?

At Q8_0 it needs about 34.8 GB. The M5 Ultra 256GB has 192 GB usable.

How fast is GLiDE 27B on the M5 Ultra 256GB?

It makes one decision in about 315 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.