on

Can the Apple M5 Pro (64 GB) run Apriel 1.5 15B Thinker?

68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 15.1 GB of 48.0 GB usable, with an estimated speed of 21.8 tok/s.

Best quant
Q4_K_M
Generation speed
21.8 tok/s
Prompt processing
392 tok/s
First token (1K prompt)
2.6 s
Largest safe context
128K

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

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX · use the MLX build

  • Runs the MLX build, typically 10–20% faster than GGUF for a model with 14.9B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open Apriel 1.5 15B Thinker in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • MLX LM Runs the MLX build, typically 10–20% faster than GGUF for a model with 14.9B active parameters on Apple silicon.

Want an agent? Apriel 1.5 15B Thinker supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Apriel 1.5 15B Thinker

Recommended for the Apple M5 Pro (64 GB): Q4_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Apriel-1.5-15b-Thinker-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Apriel-1.5-15b-Thinker-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker (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 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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 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.30.8 / 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.11.1 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.7.3 / 15
Match score74.1 / 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 12.0 GB 48.0 GB 35.9 tok/s Runs great 50Fair quality (C) · 50 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 13.3 GB 48.0 GB 28.2 tok/s Runs well 60Good quality (B) · 60 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 15.1 GB 48.0 GB 21.8 tok/s Runs well 68Good quality (B) · 68 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 16.6 GB 48.0 GB 18.0 tok/s Runs well 69Good quality (B) · 69 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 18.2 GB 48.0 GB 15.2 tok/s Runs well 71Great quality (A) · 71 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 21.8 GB 48.0 GB 11.1 tok/s Runs well 72Great quality (A) · 72 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 35.8 GB 48.0 GB 5.1 tok/s Slow 72Great quality (A) · 72 of 100: how capable the model is at F16, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) 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

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.

Was this page helpful?