LLMs · Cisco Foundation AI

Foundation-Sec 1.1 8B Instruct

Name: Foundation-Sec 1.1 8B Instruct
Author: Cisco Foundation AI

Parameters: 8.0B
Architecture: Dense
Max context: 128K tokens
License: llama3.1
Released: 2025-11

How to run it

Quantized files · GGUF

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

huggingface.co/mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/fdtn-ai/Foundation-Sec-1.1-8B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

Apps that run Foundation-Sec 1.1 8B Instruct

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

LM Studio

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

Also good:

Windows PC, NVIDIA GPU

LM Studio

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

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

LM Studio

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

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App	Type	Opens it as	Systems	Engine	Start
LM Studio	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, MLX	Open in LM Studio
Ollama	CLI + API	GGUF from Hugging Face	macOSWindowsLinux	llama.cpp, MLX	`ollama run hf.co/mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF:Q4_K_M`
llama.cpp	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	`llama-server -hf mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF:Q4_K_M`
Jan	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, MLX	Open in Jan
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve fdtn-ai/Foundation-Sec-1.1-8B-Instruct`
SGLang	Server	Original weights	WSLWindowsLinux	SGLang	`python3 -m sglang.launch_server --model-path fdtn-ai/Foundation-Sec-1.1-8B-Instruct`
Docker Model Runner	CLI + API	GGUF	macOSWindowsLinux	llama.cpp, vLLM	`docker model run hf.co/mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF:Q4_K_M`
Lemonade	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, ONNX Runtime GenAI	`lemonade pull mradermacher/Foundation-Sec-1.1-8B-Instruct-GGUF:Q4_K_M`
Unsloth Desktop	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, MLX, stable-diffusion.cpp	Open in Unsloth Desktop
Atomic Chat	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, MLX-VLM	Open in Atomic Chat
GhidrAssist	RE plugin	GGUF	macOSWindowsLinux	uses Ollama, LM Studio or any OpenAI-compatible server	Add it to Ghidra, then point it at a local model server
Gepetto	RE plugin	GGUF	macOSWindowsLinux	uses Ollama or an OpenAI-compatible server	Add it to IDA Pro, then point it at a local model server
BinAssist	RE plugin	GGUF	macOSWindowsLinux	uses Ollama, LM Studio or any OpenAI-compatible server	Add it to Binary Ninja, then point it at a local model server
r2ai and decai	RE plugin	GGUF	macOSWindowsLinux	uses Ollama, llama.cpp or LM Studio	Add it to radare2, then point it at a local model server
KoboldCpp	Desktop app	GGUF	macOSWindowsLinux	llama.cpp	Get KoboldCpp
Text Generation WebUI	Desktop app	GGUF	macOSWindowsLinux	llama.cpp, Transformers, ExLlamaV3	Get Text Generation WebUI
llamafile	CLI + API	GGUF	macOSWindowsLinux	llama.cpp	Get llamafile
Msty Studio	Desktop app	GGUF	macOSWindowsLinux	Ollama, llama.cpp, MLX	Open in Msty Studio
Llama (macOS)	Desktop app	GGUF	macOS	llama.cpp	Open in Llama (macOS)
RecurseChat	Desktop app	GGUF	macOS	llama.cpp	Open in RecurseChat
node-llama-cpp	Library	GGUF	macOSWindowsLinux	llama.cpp	Get node-llama-cpp
PocketPal AI	Mobile app	GGUF	iOSAndroid	llama.cpp	App Store Google Play
ChatterUI	Mobile app	GGUF	Android	llama.cpp	Download the APK
LLM Farm	Mobile app	GGUF	iOS	llama.cpp	Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization	Quality	Weights	Total at 8K context	Total at 32K context	Download
`Q2_K`	Low	3.2 GB	4.9 GB	8.1 GB	GGUF (opens in a new tab)
`Q3_K_M`	Low	3.9 GB	5.6 GB	8.8 GB	GGUF (opens in a new tab)
`Q4_K_M`	Good	4.9 GB	6.6 GB	9.8 GB	GGUF (opens in a new tab)
`Q5_K_M`	High	5.7 GB	7.4 GB	10.6 GB	GGUF (opens in a new tab)
`Q6_K`	High	6.6 GB	8.3 GB	11.5 GB	GGUF (opens in a new tab)
`Q8_0`	Very high	8.5 GB	10.2 GB	13.4 GB	GGUF (opens in a new tab)
`F16`	Full	16.1 GB	17.7 GB	21.0 GB	GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Foundation-Sec 1.1 8B Instruct

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at Q3_K_M.

Quality	Hardware	Quant	Memory	Speed	Runs
50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 9070 XT	`Q8_0`	11.0 GB / 16.0 GB	42.9 tok/s	Runs great
50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 Ti SUPER	`Q8_0`	11.0 GB / 16.0 GB	52.3 tok/s	Runs great
50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4080 SUPER	`Q8_0`	11.0 GB / 16.0 GB	57.3 tok/s	Runs great
50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5070 Ti	`Q8_0`	11.0 GB / 16.0 GB	69.7 tok/s	Runs great
50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5080	`Q8_0`	11.0 GB / 16.0 GB	74.7 tok/s	Runs great
49Fair quality (C) · 49 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 SUPER	`Q6_K`	8.9 GB / 12.0 GB	53.5 tok/s	Runs great
49Fair quality (C) · 49 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 3060 12GB	`Q6_K`	8.9 GB / 12.0 GB	38.2 tok/s	Runs great
48Fair quality (C) · 48 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	Intel Arc B580	`Q5_K_M`	8.0 GB / 12.0 GB	44.0 tok/s	Runs great
50Fair quality (C) · 50 of 100: how capable the model is at F16, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5090	`F16`	19.3 GB / 32.0 GB	64.3 tok/s	Runs great
47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4060	`Q4_K_M`	7.0 GB / 8.0 GB	41.6 tok/s	Runs welltight fit

Check it on your own hardware

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.