Can the Apple M5 Pro (64 GB) run GLiDE 0.8B?

Yes, it runs great

Runs great. At F16 it needs about 6.0 GB of 48.0 GB usable, with an estimated speed of 102 tok/s.

Best quant: F16
Generation speed: 102 tok/s
Prompt processing: 7,771 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 256K

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

On macOS

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

Handy if you already work in Docker.

docker model run hf.co/fastino/GLiDE-0.8B

Text Generation WebUI

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 0.8B

Original weights · Hugging Face

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

huggingface.co/fastino/GLiDE-0.8B (opens in a new tab)

No sign-in needed.

Links checked 4 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (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 Pro (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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	5.0 / 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.	2.9 / 15
Match score	47.9 / 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 Pro (64 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	4.8 GB	48.0 GB	711 tok/s	Runs great	7Basic quality (D) · 7 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q3_K_M`	4.9 GB	48.0 GB	559 tok/s	Runs great	9Basic quality (D) · 9 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q4_K_M`	5.0 GB	48.0 GB	433 tok/s	Runs great	10Basic quality (D) · 10 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q5_K_M`	5.1 GB	48.0 GB	357 tok/s	Runs great	10Basic quality (D) · 10 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	5.2 GB	48.0 GB	300 tok/s	Runs great	10Basic quality (D) · 10 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q8_0`	5.3 GB	48.0 GB	220 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	6.0 GB	48.0 GB	102 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 104 tok/s · Runs great
11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 134 tok/s · Runs great
11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB F16 · 138 tok/s · Runs great
11Basic quality (D) · 11 of 100: how capable the model is at F16, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 194 tok/s · Runs great
11Basic quality (D) · 11 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 (16 GB) Q8_0 · 49.0 tok/s · Runs great
11Basic quality (D) · 11 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) Q8_0 · 86.1 tok/s · Runs great

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.