LLMs ·

Sage-A1.5 35B-A3B

Parameters
36B (3.0B active)
Architecture
Mixture of experts
Max context
256K tokens
License
apache-2.0
Released
2026-10

How to run it

Quantized files · GGUF

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

huggingface.co/mradermacher/Sage-A1.5-i1-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/Sage-A1.5-i1-GGUF

Original weights · Hugging Face

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

huggingface.co/InternScience/Sage-A1.5 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from InternScience. The weights themselves download from Hugging Face.

github.com/InternScience/Agents-A1 (opens in a new tab)

Official repo linked from the Sage-A1.5 model card; its README currently documents Agents-A1 and the shared evaluation framework.

git clone https://github.com/InternScience/Agents-A1

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

Apps that run Sage-A1.5 35B-A3B

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/Sage-A1.5-i1-GGUF:Q4_K_M
llama.cpp CLI + API GGUF macOSWindowsLinux llama.cpp 
llama-server -hf mradermacher/Sage-A1.5-i1-GGUF:Q4_K_M
Jan Desktop app GGUF macOSWindowsLinux llama.cpp, MLX Open in Jan
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve InternScience/Sage-A1.5
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path InternScience/Sage-A1.5
Docker Model Runner CLI + API GGUF macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/mradermacher/Sage-A1.5-i1-GGUF:Q4_K_M
Lemonade Desktop app GGUF macOSWindowsLinux llama.cpp, ONNX Runtime GenAI 
lemonade pull mradermacher/Sage-A1.5-i1-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
Pi Coding agent GGUF macOSWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
Hermes Agent Assistant GGUF macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenClaw Assistant GGUF macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenCode Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Codex CLI Coding agent GGUF macOSWSLWindowsLinux uses Ollama or any OpenAI-compatible server Point it at a llama.cpp or MLX LM server
OpenHands Coding agent GGUF macOSWSLWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Zed Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Cline Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open Interpreter Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Goose Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Aider Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Crush Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Qwen Code Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Kilo Code Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open WebUI Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Khoj Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Letta Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM 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
Continue Coding agent GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
SillyTavern Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
LibreChat Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Cherry Studio Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Chatbox Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PrivateGPT Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
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.

KV cache: hybrid: only the full-attention layers (1 in 4) keep a KV cache; the rest use linear attention.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 14.4 GB 15.1 GB 15.7 GB GGUF (opens in a new tab)
Q3_K_M Low 17.6 GB 18.4 GB 18.9 GB GGUF (opens in a new tab)
Q4_K_M Good 21.9 GB 22.7 GB 23.2 GB GGUF (opens in a new tab)
Q5_K_M High 25.5 GB 26.3 GB 26.8 GB GGUF (opens in a new tab)
Q6_K High 29.5 GB 30.2 GB 30.8 GB GGUF (opens in a new tab)
Q8_0 Very high 38.1 GB 38.9 GB 39.4 GB GGUF files (opens in a new tab)
F16 Full 71.9 GB 72.7 GB 73.2 GB Original weights (opens in a new tab)

Best hardware for Sage-A1.5 35B-A3B

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7600 XT (launch price $329) at Q2_K.

Quality Hardware Quant Memory Speed Runs
91Excellent quality (A+) · 91 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Workstation EditionNVIDIA RTX PRO 6000 Blackwell F16 77.5 GB / 96.0 GB 98.2 tok/s Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple MacBook Pro (14-inch, 2021) (M1 Pro or M1 Max)Apple M1 Max (64 GB) Q6_K 34.1 GB / 48.0 GB 72.3 tok/s Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Apple MacBook Pro (14-inch, M4 Pro or M4 Max, 2024)Apple M4 Max (64 GB) Q6_K 34.1 GB / 48.0 GB 90.1 tok/s Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. Apple MacBook Pro (14-inch, M4 Pro or M4 Max, 2024)Apple M4 Pro (48 GB) Q5_K_M 29.2 GB / 36.0 GB 62.2 tok/s Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Founders EditionNVIDIA GeForce RTX 5090 Q6_K 31.8 GB / 32.0 GB 179 tok/s Runs welltight fit
91Excellent quality (A+) · 91 of 100: how capable the model is at F16, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. Apple Mac Studio (2023) (M2 Max or M2 Ultra)Apple M2 Ultra (192 GB) F16 84.2 GB / 144 GB 51.3 tok/s Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Sapphire NITRO+ AMD Radeon RX 7900 XTX Vapor-X 24GBAMD Radeon RX 7900 XTX Q4_K_M 23.9 GB / 24.0 GB 158 tok/s Runs welltight fit
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. ASUS ROG Strix GeForce RTX 3090 OC Edition 24GB GDDR6XNVIDIA GeForce RTX 3090 Q4_K_M 23.9 GB / 24.0 GB 167 tok/s Runs welltight fit
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Founders EditionNVIDIA GeForce RTX 4090 Q4_K_M 23.9 GB / 24.0 GB 173 tok/s Runs welltight fit
91Excellent quality (A+) · 91 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Apple MacBook Pro (14-inch, M4 Pro or M4 Max, 2024)Apple M4 Max (128 GB) Q8_0 46.6 GB / 96.0 GB 71.4 tok/s Runs great

Check it on your own hardware

Run it on a cluster

Devices that can’t run Sage-A1.5 35B-A3B alone can run it together. These example clusters do:

Try it with your own devices

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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