on

Can the Apple M5 Ultra (256 GB) run Solar Open 100B?

80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 126 GB of 192 GB usable, with an estimated speed of 44.7 tok/s.

Best quant
Q8_0
Generation speed
44.7 tok/s
Prompt processing
1,948 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the Apple M5 Ultra (256 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 12B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open Solar Open 100B 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 12B active parameters on Apple silicon.

Want an agent? Solar Open 100B 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 Solar Open 100B

Quantized files · GGUF

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

huggingface.co/mradermacher/Solar-Open-100B-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Solar-Open-100B-i1-GGUF

Original weights · Hugging Face

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

huggingface.co/upstage/Solar-Open-100B (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (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 great
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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.36.0 / 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.15.0 / 15
Match score91.0 / 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
Q2_K 58.6 GB 192 GB 113 tok/s Runs great 56Good quality (B) · 56 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 67.9 GB 192 GB 94.7 tok/s Runs great 67Good quality (B) · 67 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 80.2 GB 192 GB 78.0 tok/s Runs great 76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 90.5 GB 192 GB 66.9 tok/s Runs great 77Great quality (A) · 77 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 102 GB 192 GB 58.0 tok/s Runs great 79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 126 GB 192 GB 44.7 tok/s Runs great 80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 223 GB 192 GB 19.0 tok/s Slowfar over GPU memory limit 80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (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 Solar Open 100B need on the M5 Ultra 256GB?

At Q8_0 it needs about 126 GB. The M5 Ultra 256GB has 192 GB usable.

How fast is Solar Open 100B on the M5 Ultra 256GB?

About 44.7 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Solar Open 100B should I use on the M5 Ultra 256GB?

Q8_0. 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.

Was this page helpful?