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Can the Apple M2 (8 GB) run Foundation-Sec 8B Reasoning?

36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (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 5.3 GB of 5.4 GB usable, with an estimated speed of 21.6 tok/s.

Best quant
Q2_K
Generation speed
21.6 tok/s
Prompt processing
278 tok/s
First token (1K prompt)
3.7 s
Largest safe context
8K

Best app to run it on the Apple M2 (8 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Foundation-Sec 8B Reasoning in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

Reverse engineering? Serve Foundation-Sec 8B Reasoning locally and use it from Ghidra, IDA Pro, Binary Ninja, radare2 or JADX with one of these:

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 Foundation-Sec 8B Reasoning

Recommended for the Apple M2 (8 GB): Q2_K. 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 Q2_K file (opens in a new tab)

huggingface.co/mradermacher/Foundation-Sec-8B-Reasoning-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Foundation-Sec-8B-Reasoning-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning (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 36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (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 (8 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.16.4 / 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.11.0 / 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 score66.4 / 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 (8 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 5.3 GB 5.4 GB 21.6 tok/s Runs welltight fit 36Basic quality (D) · 36 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 6.0 GB 5.4 GB 14.5 tok/s Slowfar over GPU memory limit 43Fair quality (C) · 43 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 7.0 GB 5.4 GB — Won't run 49Fair quality (C) · 49 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 7.8 GB 5.4 GB — Won't run 50Fair quality (C) · 50 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 8.7 GB 5.4 GB — Won't run 51Fair quality (C) · 51 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 10.6 GB 5.4 GB — Won't run 52Fair quality (C) · 52 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 18.2 GB 5.4 GB — Won't run 52Fair quality (C) · 52 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (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 Foundation-Sec 8B Reasoning need on the M2 8GB?

At Q2_K it needs about 5.3 GB. The M2 8GB has 5.4 GB usable.

How fast is Foundation-Sec 8B Reasoning on the M2 8GB?

About 21.6 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Foundation-Sec 8B Reasoning should I use on the M2 8GB?

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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