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Can the Apple M2 Ultra (192 GB) run Naive N0.5 Flash (309B-A15.5B)?

66Good quality (B) · 66 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q2_K it needs about 136 GB of 144 GB usable, with an estimated speed of 70.0 tok/s.

Best quant
Q2_K
Generation speed
70.0 tok/s
Prompt processing
1,088 tok/s
First token (1K prompt)
0.9 s
Largest safe context
264K

Best app to run it on the Apple M2 Ultra (192 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/NaiveAI/Naive-N0.5-Flash

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 Naive N0.5 Flash (309B-A15.5B)

Original weights · Hugging Face

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

huggingface.co/NaiveAI/Naive-N0.5-Flash (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Naive AI. The weights themselves download from Hugging Face.

github.com/NaiveAI-Labs/Naive-N0.5-Flash (opens in a new tab)

git clone https://github.com/NaiveAI-Labs/Naive-N0.5-Flash

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

How it’s rated

Quality 66Good quality (B) · 66 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 83/100 (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 Runs welltight fit
How well it runs on the Apple M2 Ultra (192 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.29.9 / 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.24.0 / 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.15.0 / 15
Match score83.9 / 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 M2 Ultra (192 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 136 GB 144 GB 70.0 tok/s Runs welltight fit 66Good quality (B) · 66 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 164 GB 144 GB 48.8 tok/s Slowfar over GPU memory limit 79Great quality (A) · 79 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 201 GB 144 GB — Won't run 90Excellent quality (A+) · 90 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 232 GB 144 GB — Won't run 92Excellent quality (A+) · 92 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 266 GB 144 GB — Won't run 94Excellent quality (A+) · 94 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 340 GB 144 GB — Won't run 95Excellent quality (A+) · 95 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 12/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 630 GB 144 GB — Won't run 95Excellent quality (A+) · 95 of 100: how capable the model is at F16, whatever it runs on. Match score 12/100 (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

Quick answers

How much memory does Naive N0.5 Flash (309B-A15.5B) need on the M2 Ultra 192GB?

At Q2_K it needs about 136 GB. The M2 Ultra 192GB has 144 GB usable.

How fast is Naive N0.5 Flash (309B-A15.5B) on the M2 Ultra 192GB?

About 70.0 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Naive N0.5 Flash (309B-A15.5B) should I use on the M2 Ultra 192GB?

Q2_K. 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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