LongCat 2.5 (1.6T-A48B) on Apple M3 (16 GB)

Can the Apple M3 (16 GB) run LongCat 2.5 (1.6T-A48B)?

No, it doesn’t fit

No. LongCat 2.5 (1.6T-A48B) needs about 714 GB even at Q2_K; the Apple M3 (16 GB) has 10.7 GB usable.

Download LongCat 2.5 (1.6T-A48B)

Original weights · Hugging Face

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

huggingface.co/meituan-longcat/LongCat-2.5 (opens in a new tab)

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

How it’s rated

Quality 68Good quality (B) · 68 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, 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 Won't run: How well it runs on the Apple M3 (16 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.	9.2 / 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.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	9.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the Apple M3 (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	714 GB	10.7 GB	—	Won't run	68Good quality (B) · 68 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q3_K_M`	874 GB	10.7 GB	—	Won't run	82Great quality (A) · 82 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q4_K_M`	1,087 GB	10.7 GB	—	Won't run	92Excellent quality (A+) · 92 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q5_K_M`	1,265 GB	10.7 GB	—	Won't run	94Excellent quality (A+) · 94 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	1,461 GB	10.7 GB	—	Won't run	96Excellent quality (A+) · 96 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q8_0`	1,888 GB	10.7 GB	—	Won't run	97Excellent quality (A+) · 97 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 13/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	3,561 GB	10.7 GB	—	Won't run	97Excellent quality (A+) · 97 of 100: how capable the model is at F16, whatever it runs on. Match score 13/100 (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.

Hardware that can run it

None. No single GPU or device in our catalog runs this model on its own; it needs several GPUs working together.

Models that do run on the Apple M3 (16 GB)

66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. Mellum2.1 Thinking (12B-A2.5B) Q4_K_M · 37.0 tok/s
76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7B Q4_K_M · 11.7 tok/s
59Good quality (B) · 59 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Ling 3.0 Tiny (8B-A1.3B) Q6_K · 46.9 tok/s
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9B Q4_K_M · 11.2 tok/s
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B Preview Q4_K_M · 10.9 tok/s

Quick answers

How much memory does LongCat 2.5 (1.6T-A48B) need on the M3 16GB?

Even at Q2_K it needs about 714 GB; the M3 16GB has 10.7 GB usable.

What can the M3 16GB run instead?

Mellum2.1 Thinking (12B-A2.5B), K2 Horizon 7B and Ling 3.0 Tiny (8B-A1.3B) run best on it; the page lists more, each with its speed.

What hardware can run LongCat 2.5 (1.6T-A48B)?

No single GPU or device in our catalog runs it on its own; it needs several GPUs working together.

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.