on

Can the Apple M3 Ultra (96 GB) run DeepHermes 3 Llama 3 8B Preview?

46Fair quality (C) · 46 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 15.9 GB of 72.0 GB usable, with an estimated speed of 54.9 tok/s.

Best quant
Q8_0
Generation speed
54.9 tok/s
Prompt processing
2,193 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the Apple M3 Ultra (96 GB)

On macOS

LM Studio

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

  • Runs the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.
  • Point-and-click: search, download and chat in one window.

Open DeepHermes 3 Llama 3 8B Preview in LM Studio

  • MLX LM Runs the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.
  • Jan Runs the MLX build, typically 20–35% faster than GGUF for a model with 8B active parameters on Apple silicon.

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 DeepHermes 3 Llama 3 8B Preview

Quantized files · GGUF

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

huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview-GGUF (opens in a new tab)

ollama run hf.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview-GGUF

Original weights · Hugging Face

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

huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview (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 46Fair quality (C) · 46 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (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 M3 Ultra (96 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.20.7 / 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.5.1 / 15
Match score65.8 / 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 M3 Ultra (96 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 10.6 GB 72.0 GB 177 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 11.4 GB 72.0 GB 139 tok/s Runs great 38Basic quality (D) · 38 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 12.3 GB 72.0 GB 108 tok/s Runs great 43Fair quality (C) · 43 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q5_K_M 13.1 GB 72.0 GB 89.1 tok/s Runs great 44Fair quality (C) · 44 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 14.0 GB 72.0 GB 74.8 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 15.9 GB 72.0 GB 54.9 tok/s Runs great 46Fair quality (C) · 46 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 23.5 GB 72.0 GB 25.3 tok/s Runs well 46Fair quality (C) · 46 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. 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

Quick answers

How much memory does DeepHermes 3 Llama 3 8B Preview need on the M3 Ultra 96GB?

At Q8_0 it needs about 15.9 GB. The M3 Ultra 96GB has 72.0 GB usable.

How fast is DeepHermes 3 Llama 3 8B Preview on the M3 Ultra 96GB?

About 54.9 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of DeepHermes 3 Llama 3 8B Preview should I use on the M3 Ultra 96GB?

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?