Both are Apache 2.0, both support tools + thinking + 262K context. The 35B MoE is the better default for most BatiFlow users because chat feels snappier; this 27B dense is the quality ceiling when throughput doesn't matter.

Available Quantizations

imatrix is applied to every low/mid-bit quant (IQ and Q4_K_M) using wikitext-2-raw calibration — consistent recipe across the BatiAI lineup.

Also on Hugging Face only:

• mmproj-Qwen-Qwen3.6-27B-Q6_K.gguf / mmproj-Qwen-Qwen3.6-27B-BF16.gguf — vision projector (see Multimodal mode)
• imatrix.dat — importance-matrix calibration data; use it to roll your own quants from the upstream BF16

Why Qwen 3.6 27B Dense?

Upstream positions the 27B as "flagship coding in a dense package" — the model is the efficient-inference companion to the 35B-A3B MoE, tuned to hold up on agentic coding when you need dense-model reliability for long-horizon tool use.

Upstream benchmarks (Alibaba official BF16 figures)

Numbers from the official Qwen3.6-27B model card. Post your own Mac bench results and we'll add them here.

Coding & Agentic

We'll fill in Alibaba's BF16 numbers after the upstream benchmark sheet is finalized; until then see MarkTechPost summary.

Key capabilities

• Agentic coding — tuned for repo-level reasoning, multi-step tool-use flows
• 262 K native context, extensible to 1,010,000 tokens via YaRN
• Thinking mode (default ON) — <think>…</think> block before final response
• Tool calling via qwen3_coder parser — works with BatiFlow's Tools API
• Multimodal (vision + video understanding) via separate mmproj file — see below
• Apache 2.0 — commercial use permitted

RAM Requirements (on-device, Dense 27B)

Dense 27B note: on every forward pass all 27B params are active. Expect slower per-token speed than typical MoE models. On Mac, realistic Qwen 3.6-27B usage starts at 24 GB unified memory — below that, single-turn inference is bottlenecked by swap.

On-device Benchmarks (measured)

Measured with BatiAI's bench harness (test-qwen3.6-27b.sh) on real hardware. Run on your Mac and share the JSON — we'll add your row.

Apple Silicon (M4 Max / mini — ollama run --verbose)

Measured with ollama serve thinking-ON (model default). ./test-qwen3.6-27b.sh on the BatiAI repo reproduces these rows in 10 minutes per Mac.

²Tool-call note: first Mac bench returned empty JSON for IQ4/Q4 — not a quant quality issue. Ollama's default think:true produced long <think> blocks on higher-quality quants that consumed the 100-token test budget before the JSON appeared. Server-side retest with --reasoning off confirmed all 5 quants emit the exact canonical JSON {"name":"send_message","arguments":{"recipient":"John","message":"hello"}}. The test-qwen3.6-27b.sh script now passes "think": false on tool-call turns (matching real BatiFlow flows). Pull the updated script for clean green checks.

⚠ IQ4_XS is currently slow on Apple Metal — use Q4_K_M for now

IQ4_XS averaged 5.52 t/s on M4 Max vs Q4_K_M at 16.56 t/s (similar file size, same machine). This appears to be an upstream llama.cpp regression on Apple M-series Metal — llama.cpp issue #21655 reports a ~3.8× IQ4_XS slowdown on M4 between tags b8680 → current, with the same quant running at expected speed on older builds and on NVIDIA (within 10 % of Q4_K_M on RTX 6000 Ada: 85.7 vs 79.0 t/s). This is a runtime-side issue, not a model-file issue — when upstream fixes the Metal IQ4 kernel, existing :iq4 GGUFs will speed up without re-quantization.

Until that fix lands, recommendation on Apple Silicon:

• :q4 (Q4_K_M) — best speed/quality on M-series Mac (16+ t/s)
• :q3 or :iq3 — smaller footprint if VRAM is tight
• :iq4 — only on NVIDIA (where it matches Q4_K_M speed)
• :q6 — quality ceiling on 32 GB+

❌ Qwen 3.6-27B does not fit usefully on 16 GB Macs

On base M4 Mac mini 16 GB, IQ3_XXS (11 GB) runs at 0.02 t/s — ~30 minutes to generate a short greeting because unified memory overflows into swap once model + KV cache + macOS + Ollama exceed 16 GB. Larger quants do not load at all.

If your Mac is 16 GB: this model is not for you. The 27B Dense (and even the 35B-A3B MoE sibling) pushes past what 16 GB unified memory can stream without swap thrash. For 16 GB Macs consider smaller BatiAI models such as Gemma 4 E4B-it, Qwen 3.5 9B, or any ~4-8 B class model.

Server reference (BatiAI build rig: 2× RTX 6000 Ada 48 GB, 96 GB total VRAM)

Not our target platform, but a useful ceiling. Configs: llama-cli --reasoning off, Linux, llama.cpp build bafae2765.

Single-GPU (CUDA_VISIBLE_DEVICES=1, where quantized GGUFs were verified)

Tool-call JSON and Korean greeting (안녕하세요!) verified on every quant server-side.

Dual-GPU tensor-split (CUDA_VISIBLE_DEVICES=0,1)

Takeaway: splitting a 27 B model that already fits in one 48 GB card is ~45 % slower than packing it on one. Multi-GPU tensor-split helps when the model is too large for a single card (e.g. Qwen 3.6-35B-A3B IQ4_XS 18 GB + long context, or 1 T+ MoE models like Kimi K2.6 that need both cards); it hurts for 27 B. Use CUDA_VISIBLE_DEVICES=1 (or =0) for fastest inference on this lineup.

Full report: Qwen-Qwen3.6-27B-20260423.md.

Takeaway: Mac vs server

Mac reaches ~20 % of the server's throughput on Dense 27B, as expected for memory-bandwidth-bound inference. This 27B is the pick when per-token latency matters less than single-pass dense-reasoning quality (batch tool-use agents, code-review loops, offline generation). For interactive streaming chat, wait for the Metal IQ4 fix to land upstream or pull :q4 (K-quant) which currently delivers the best Mac speed/quality combo at 16.5 t/s on M4 Max.

Try it yourself

ollama run batiai/qwen3.6-27b:iq4 --verbose "Write a haiku about Seoul in autumn."

Full harness (cold start, 3× warm, long response, Korean, tool call, memory delta):

./test-qwen3.6-27b.sh          # from the batiai-models repo, or download just this script

Share reports/bench-qwen3.6-27b-*.json and we'll add your hardware row.

Multimodal mode (opt-in)

Upstream Qwen 3.6-27B is multimodal (text + image + video). GGUF delivers this as two files: main model.gguf (text tower) and mmproj.gguf (vision projector). This repo ships both, separate, so you pick:

Multimodal usage (llama.cpp)

wget https://huggingface.co/batiai/Qwen3.6-27B-GGUF/resolve/main/Qwen-Qwen3.6-27B-IQ4_XS.gguf
wget https://huggingface.co/batiai/Qwen3.6-27B-GGUF/resolve/main/mmproj-Qwen-Qwen3.6-27B-Q6_K.gguf

# Server mode (OpenAI-compatible Vision API)
llama-server \
  -m Qwen-Qwen3.6-27B-IQ4_XS.gguf \
  --mmproj mmproj-Qwen-Qwen3.6-27B-Q6_K.gguf \
  -c 32768 --host 127.0.0.1 --port 8080

# One-shot CLI
llama-mtmd-cli \
  -m Qwen-Qwen3.6-27B-IQ4_XS.gguf \
  --mmproj mmproj-Qwen-Qwen3.6-27B-Q6_K.gguf \
  --image ~/Desktop/photo.jpg \
  -p "describe this image"

mmproj variants

(Q8_0 is unavailable for this mmproj — some Qwen 3.6 vision tensors have shapes incompatible with Q8_0's column-32 alignment; Q6_K's K-quant block layout handles them.)

Technical Details

• Original Model: Qwen/Qwen3.6-27B
• Released: 2026-04-22
• Architecture: Dense 27B + Gated DeltaNet / Gated Attention hybrid
  • 64 layers, hidden 5120, FFN intermediate 17,408
  • Layer pattern: 16 × (3× linear-attention + 1× full-attention) — native softmax attention every 4 layers
  • Linear-attention heads: 48 V / 16 QK (head dim 128)
  • Softmax-attention heads: 24 Q / 4 KV (head dim 256, RoPE dim 64)
• Parameters: 27 B total, 27 B active per forward pass (dense — no expert routing)
• Context Window: 262,144 tokens native (extensible to ~1,010,000 via YaRN)
• Vocabulary: 248,320 tokens (padded)
• Multimodal: vision encoder + mmproj (image/video understanding)
• Modes: thinking / non-thinking switchable (thinking is default ON)
• License: Apache 2.0
• Quantized by: BatiAI
• Calibration data: wikitext-2-raw (imatrix.dat included on HF)

How We Quantize

Qwen/Qwen3.6-27B (BF16 safetensors, ~54 GB)
  ↓ llama.cpp convert_hf_to_gguf.py  (text tower)
BF16 GGUF (~54 GB)
  ↓ llama.cpp convert_hf_to_gguf.py --mmproj  (vision tower, separate)
mmproj-BF16.gguf
  ↓ llama-imatrix  (wikitext-2-raw, GPU-accelerated)
imatrix.dat
  ↓ llama-quantize --imatrix  (IQ3_XXS, Q3_K_M, IQ4_XS, Q4_K_M)
  ↓ llama-quantize             (Q6_K, mmproj Q6_K)
Quantized GGUF
  ↓ ollama push  +  hf upload
Published to batiai/ on Ollama & Hugging Face

No third-party intermediaries. Direct from official Qwen weights.

About the "3.6" naming

Upstream calls this Qwen 3.6 publicly. Internally the HF config registers the architecture as Qwen3_5ForConditionalGeneration (transitional class name from the 3.5 line). llama.cpp handles this via Qwen3_5TextModel — the same converter path used for the 35B-A3B MoE sibling.

About BatiFlow

BatiFlow is a macOS-native AI automation app — 5 MB, Swift-native. Free on-device AI via Ollama — no API costs, no usage limits, 100 % private.

• AI Command Bar — natural-language action execution
• KakaoTalk / iMessage / Slack automation
• Chrome navigation, filling, screenshots via CDP
• 57 built-in tools — calendar, mail, reminders, files, shell, etc.
• Skill builder — reusable YAML automations
• Multilingual — Korean / English

Download BatiFlow

More from BatiAI

Same recipe (official weights → imatrix GGUF → verified on Mac), across the lineup:

Full org: huggingface.co/batiai

License

This repo mirrors the upstream license. Qwen/Qwen3.6-27B is released under Apache 2.0 — commercial use permitted.

BatiAI's quantization pipeline is MIT.

Sources

Benchmark numbers in this card come from the official upstream Qwen/Qwen3.6-27B model card and coverage at MarkTechPost and Let's Data Science. Quantization and on-device numbers are measured by BatiAI.

Benchmarks