LLMs ·

Qwen3.5 27B

Parameters
28B
Architecture
Dense
Max context
256K tokens
License
apache-2.0
Released
2026-02

How to run it

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run qwen3.5:27b

Quantized files · GGUF

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

huggingface.co/unsloth/Qwen3.5-27B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.5-27B-GGUF

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.5-27B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/QwenLM/Qwen3.8 (opens in a new tab)

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

git clone https://github.com/QwenLM/Qwen3.8

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Qwen3.5 27B

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

Runs the MLX build, typically up to 10% faster than GGUF for a model with 27.8B active parameters on Apple silicon.

Also good:

Windows PC, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

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

Ollama

One command with Ollama’s own build of this model.

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 or MLX macOSWindowsLinux llama.cpp, MLX Open in LM Studio
Ollama CLI + API Ollama library macOSWindowsLinux llama.cpp, MLX 
ollama run qwen3.5:27b
llama.cpp CLI + API GGUF macOSWindowsLinux llama.cpp 
llama-server -hf unsloth/Qwen3.5-27B-GGUF:Q4_K_M
Jan Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX Open in Jan
MLX LM CLI + API MLX macOS MLX 
mlx_lm.chat --model mlx-community/Qwen3.5-27B-4bit
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve Qwen/Qwen3.5-27B
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path Qwen/Qwen3.5-27B
Docker Model Runner CLI + API GGUF macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/unsloth/Qwen3.5-27B-GGUF:Q4_K_M
Lemonade Desktop app GGUF macOSWindowsLinux llama.cpp, ONNX Runtime GenAI 
lemonade pull unsloth/Qwen3.5-27B-GGUF:Q4_K_M
Unsloth Desktop Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX, stable-diffusion.cpp Open in Unsloth Desktop
Atomic Chat Desktop app GGUF or MLX macOSWindowsLinux llama.cpp, MLX-VLM Open in Atomic Chat
GhidrAssist RE plugin GGUF or MLX 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 or MLX 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 or MLX 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 or MLX 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 or MLX 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
Inferencer Desktop app MLX macOS MLX Get Inferencer
node-llama-cpp Library GGUF macOSWindowsLinux llama.cpp Get node-llama-cpp
exo Server MLX macOSLinux MLX Get exo
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 11.1 GB 12.2 GB 13.9 GB GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M Low 13.6 GB 14.7 GB 16.4 GB GGUF (opens in a new tab)
Q4_K_M Good 16.9 GB 18.1 GB 19.7 GB GGUF (opens in a new tab)
Q5_K_M High 19.7 GB 20.9 GB 22.5 GB GGUF (opens in a new tab)
Q6_K High 22.8 GB 23.9 GB 25.5 GB GGUF (opens in a new tab)
Q8_0 Very high 29.4 GB 30.6 GB 32.2 GB GGUF (opens in a new tab)
F16 Full 55.6 GB 56.7 GB 58.3 GB GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Qwen3.5 27B

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7600 XT (launch price $329) at Q3_K_M.

Quality Hardware Quant Memory Speed Runs
92Excellent quality (A+) · 92 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Founders EditionNVIDIA GeForce RTX 5090 Q6_K 25.5 GB / 32.0 GB 54.9 tok/s Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 94/100 (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 19.3 GB / 24.0 GB 41.4 tok/s Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Founders EditionNVIDIA GeForce RTX 4090 Q4_K_M 19.3 GB / 24.0 GB 44.5 tok/s Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 94/100 (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 19.3 GB / 24.0 GB 36.5 tok/s Runs great
93Excellent quality (A+) · 93 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Workstation EditionNVIDIA RTX PRO 6000 Blackwell Q8_0 35.4 GB / 96.0 GB 40.3 tok/s Runs great
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPERNVIDIA GeForce RTX 4080 SUPER Q3_K_M 15.5 GB / 16.0 GB 42.0 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. ASUS TUF Gaming GeForce RTX 5070 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5070 Ti Q3_K_M 15.5 GB / 16.0 GB 51.2 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Founders EditionNVIDIA GeForce RTX 5080 Q3_K_M 15.5 GB / 16.0 GB 54.8 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPERNVIDIA GeForce RTX 4070 Ti SUPER Q3_K_M 15.5 GB / 16.0 GB 38.4 tok/s Runs welltight fit
92Excellent quality (A+) · 92 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. Apple MacBook Pro (14-inch, 2021) (M1 Pro or M1 Max)Apple M1 Max (64 GB) Q6_K 27.8 GB / 48.0 GB 10.6 tok/s Runs well

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.

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