Qwen3.8-27B is a 27B parameter dense model Qwen, featuring vision and reasoning capabilities, a 256K context window, and runs locally on 17GB RAM/VRAM setups. Qwen3.8 excels at agentic coding, vision and chat tasks, and can now run via Unsloth GGUFs, NVFP4 and Unsloth Desktop.
Nov 11 Update: Qwen3.8-27B GGUFs now use Unsloth Dynamic V3.1 which improves average accuracy by 5-10% accuracy at comparable sizes including Terminal Bench, DeepSWE and more benchmarks. We're working on an even better version of Dynamic quants which will be applied to Qwen4 and future models.
Dynamic V3.1 Update (November 11th 2026)
We're excited to release new Qwen3.8-27B GGUFs using our updated Unsloth Dynamic V3.1 (preview) algorithm which improves Unsloth quants by 5-10% accuracy at the same size on most benchmarks and is the best in class for most tasks including coding, agentic workloads and general chat.
Most quants from 2bit until 6bit are updated. We found 3bit UD3 quants to do on par with UD3.1, so we do not update them. We are actively working on making all quants better!
We ran agentic benchmarks including Terminal Bench 2.1, DeepSWE v1.1, and general benchmarks like LiveCodeBench v6, GPQA Diamond, MMLU-Pro, AIME25 and many others. We used 256K context lengths for all agentic benchmarks, and 128K for the rest. We followed the same methodology for Qwen3.8-27B as DeepSeek-V4.1-Flash and Qwen3.8-Flash-Next which also utilized our V3.1 preview algorithm.
Dynamic V3.1 Full Breakdown
Click below to see full details of benchmarking and more:
⚙️ Usage Guide
Qwen3.8-27B Requirements:
Qwen3.8-27B 4-bit quants work on 16-19GB VRAM like RTX 5080, 4090 or a Mac with 24GB RAM. Table: Hardware requirements (units = total memory: RAM + VRAM, or unified memory)
Recommended Settings
Qwen3.8-27B is a hybrid thinking model with different default settings for thinking and non-thinking modes. Extra high is enabled by default so if you want shorter thinking traces, you can adjust the thinking effort:
Maximum context window:
262,144(can be extended to 1M via YaRN)Thinking Mode:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0Instruct (or non-thinking) mode:
temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0
💡 Thinking + Preserve Thinking
Qwen3.8 has Preserve Thinking which leaves the thinking trace from the previous conversation. This increases the number of tokens you use, but could increase accuracy in continued conversations. Unsloth has 'Think' and Preserved Thinking toggles for Qwen3.8 (see right):
Qwen3.8-27B comes with support for reasoning_effort, which can be used to adjust reasoning depth and control cost. These toggles are automatically enabled in Unsloth:
xhigh(default): for complex tasks demanding thorough analysismedium: balancing accuracy and speedlow: efficient reasoning optimizing for speed and cost
Run Qwen3.8 Guide
You can now run Qwen3.8-27B in llama.cpp and Unsloth Desktop. Feel free to change quantization type.
🦥 Run Qwen3.8 in Unsloth Desktop
Qwen3.8 can run in Unsloth Desktop, an open-source UI app for local AI. Unsloth automatically offloads to RAM and detects multiGPU setups. With Unsloth Desktop, you can run models locally on MacOS, Windows, Linux and:
Search and download Qwen3.8
Go to Unsloth Chat or Model hub and search for Qwen3.8 in the search bar and download your desired model and quant.
Run Qwen3.8
Inference parameters should be auto-set when using Unsloth, however you can still change it manually. You can also edit the context length, chat template and other settings.
For example using Unsloth Desktop with the 397GB Qwen3.8 (-91% smaller) allows you to toggle thinking modes, allow inline canvas, web search and code execution and much more.
Serve Qwen3.8 with Unsloth API
You can use unsloth run command and serve Qwen3.8 via an API using llama-server runtime flags, including context sizing, GPU layers, threading, sampling, networking, and tool configuration. For more info see our API docs or unsloth start.
🦙 Run Qwen3.8 in llama.cpp
Download the latest version of llama.cpp. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. For Apple Mac / Metal devices, set -DGGML_CUDA=OFF then continue as usual - Metal support is on by default.
To run the model in llama-cli, follow the code snippets below: Remember to change settings according to your use-case.
⚡️NVFP4
Like Qwen3.6, we’re also releasing new dynamic NVFP4 Qwen3.8-27B quants that run ~1.5× faster than BF16 checkpoints, with better performance and comparable file sizes. Run Qwen3.8-27B NVFP4 1.5x faster on 24GB VRAM. We also added FP8 KV cache calibration for 2x longer context lengths! NVFP4 requires NVIDIA's Blackwell GPUs like RTX 50X, DGX Spark (see Qwen3.8-27B), B200, B300 GPUs. For older GPUs, our GGUFs work well! You can run NVFP4 quants in vLLM only for now (SGLang is not supported).
See below for previous benchmarks conducted for Qwen3.6 as well comparing to other NVFP4 implementations which use 16bit activations vs our NVFP4 activations:
All benchmarks use 1x B200 128 concurrency. Higher concurrency can boost 35B to 17,561 tokens / s.
For accuracy benchmarks we ran KLD and Top-1 agreement over Code, Chat and many domains. NVFP4 is consistency 92% to 97% accuracy recovery vs BF16
For accuracy benchmarks For Qwen 3.6, we conducted MMLU-Pro, AIME 2025, GPQA for FP8, BF16, NVIDIA's NVFP4 and our NVFP4s - we show our faster quants do similarly on all:
vLLM:
To install vLLM in a separate venv:
Then to serve the 27B variant:
To enable MTP / speculative decoding (faster decode but somewhat less throughput), use:
If you get Torchcodec issues, be sure to do the below then relaunch vllm.
SGLang:
If using SGLang, you must use SGLang version v0.5.19 otherwise it will not work since we quantize the lm_head to FP8.
For sglang after installing the latest:
🤯Quantization Analysis
NVFP4 quants are 1.5x faster than BF16 and retains 92 to 97% top-1 accuracy.
Top-1 accuracy plot as shown in UD-3 fro Qwen3.8-27B:
And mean KLD for Qwen3.8:
📊 Benchmarks
Qwen3.8-27B
See further below for table benchmarks:
Text Performance
Qwen3.8-2.4T-A95B
Qwen3.8-2.4T-A95B is a 2.4T parameter (95B active) model with rivaling GPT-5.6 Sol. Full-precision Qwen3.8-2.4T-A95B requires 4.9TB of storage and 1-bit Unsloth Dynamic GGUFs takes 397GB (91% smaller), and larger IQ1_S takes 508GB.
⚙️ Usage Guide
Qwen3.8-2.4T Requirements:
Recommended Settings
Qwen3.8-2.4T is thinking-only, while Qwen3.8-Max is hybrid.
Context length = up to
1,010,000temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0
If the model fits, you will get ~20 tokens/s generation when using B200s and >120 tokens / s throughput. Best rule of thumb: RAM+VRAM ≈ the quant size; otherwise it’ll still work, just much slower due to disk offloading.
Run Qwen3.8-2.4T Guide
You can now run Qwen3.8 in llama.cpp and Unsloth Desktop. For the large Qwen3.8-2.T model, we will be utilizing the 397GB IQ1_XXXS quant (named Q1_0) for best results in terms of accessibility and accuracy and it will require at least 450GB RAM. Feel free to change quantization type.
🦥 Run in Unsloth Desktop
Qwen3.8 can run in Unsloth Desktop, an open-source UI app for local AI. Unsloth automatically offloads to RAM and detects multiGPU setups. With Unsloth Desktop, you can run models locally on MacOS, Windows, Linux. Go to Unsloth Chat or Model hub and search for Qwen3.8 in the search bar and download your desired model and quant. Then run it.
Qwen3.8-2.4T-A95B New 1-bit data-types
We extended IQ1_S in llama.cpp which is 1.5625 bits per weight to 1.1875 bpw by reducing the number of entries in the codebook - we found this works well for large models, and can still retain a lot of accuracy - we also found these new data-types to be fine for post training quantization (PTQ) without the need for QAT or QAD (quantization aware training / distillation). Qwen3.8-2.4T-A95B-GGUF
🦙 Run Qwen3.8-2.4T in llama.cpp
We need to use the specific IQ1_XXXS branch here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. For Apple Mac / Metal devices, set -DGGML_CUDA=OFF then continue as usual - Metal support is on by default.
Download the model via (after installing pip install huggingface_hub). You can choose Q1_0 for IQ1_XXXS or other quantized versions like Q8_0 . If downloads get stuck, see: Hugging Face Hub, XET debugging
To run the model in llama-cli, follow the code snippets below. Remember to change settings according to your use-case.
To run the general UD-IQ1_S you can do: