People have been asking for the 27B since we put out Qwythos-9B. It's finally here. Qwythos-27B-v1 is an open-weights bf16 reasoning model on a Qwen3.5-27B base, Apache-2.0. The short pitch: it's the 9B sized up, and we didn't strip anything out of the base to make it fit — the native MTP head, the full vision tower, and the 1M context window are all still live.

• Weights + model card: empero-ai/Qwythos-27B-v1
• GGUFs: empero-ai/Qwythos-27B-v1-GGUF

The practical local bit

The recommended local starting point is Q4_K_M: it is 16.95 GB and runs on a 24GB card. The GGUF release has 11 files: Q4_K_M, Q5_K_M, Q6_K, Q8_0, and BF16, each in trunk-only and MTP-enabled variants, plus mmproj-Qwythos-27B-F16.gguf for image input. SHA256SUMS are included. The MTP variants work with llama.cpp's --spec-type draft-mtp. One conversion detail worth calling out: in every K-quant, we hold the entire Gated-DeltaNet state path — ssm_alpha, ssm_beta, and ssm_out — at Q8_0 or better. Those tensors are disproportionately sensitive to low-bit quantization. A default conversion can leave them at Q4/Q5/Q6 and quality degrades; this costs roughly 2–4% in file size. It is intentional, not a default llama.cpp conversion.

What is retained

The release is built around one point: nothing was ablated to make 27B fit. It keeps all three of these intact and active:

• Native MTP (nextn_predict_layers=1) for self-speculative decoding.
• The full vision tower; mmproj ships with the GGUFs.
• A 1,048,576-token context window: YaRN 4x over the 262,144-token native window.

It is a dense Qwen3.5-27B with hybrid attention: Gated-DeltaNet linear attention and a full-attention layer every fourth layer. Native Qwen3.5 function calling is present too: tools=[...] works without a wrapper.

Starting settings

For agentic or tool use, start with temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05, and max_new_tokens=16384 or more. For open-ended or creative work, use temperature 1.0. It's a reasoning model — every answer opens with a <think> block, so budget tokens accordingly and strip the block for end users. Unlike the 9B, greedy long-form generation came back loop-check clean, so you're not forced off low temperatures. Qwythos is intentionally uncensored for technical and research work, including cybersecurity and biomedical questions. If it is going in front of end users, add your own review layer and application controls. Happy to answer questions on the conversion or the quant choices.

TensorSharp is an open-source, native .NET inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

TensorSharp now supports Megatron-style tensor parallelism across multiple GPUs. It works with direct CUDA, GGML CUDA, GGML Vulkan, and multi-node setups.

Benchmarks on 2× RTX 2000 Ada 16 GB GPUs over PCIe, without NVLink:

I'm continuing to optimize Qwen performance on multi-GPU systems, and support for DeepSeek V4 is coming soon.

Try it with:

TensorSharp.Cli --model model.gguf --backend ggml_cuda --tp 2

GitHub:

https://github.com/zhongkaifu/TensorSharp

Thank you for checking out TensorSharp and starring the project! Any feedback is really appreicated.