Qwen3.8-27B-oQ4e-mtp

4-bit MLX quantization of Qwen/Qwen3.8-27B (hybrid Gated DeltaNet + Gated Attention, native vision, 262K context, thinking mode with preserved reasoning). The smallest build of the family: fits 32 GB-class Apple Silicon, and on 64 GB machines it's the long-context option — the ~5 GB saved vs. the 6-bit goes to KV cache headroom under the default GPU wired-memory limit. Made with oQ (oMLX v0.6.0.dev1) mixed-precision quantization.

Sibling repos:

• Qwen3.8-27B-oQ4e-fp16-mtp — 4-bit / fp16, ~16.7 GiB, tuned for M1/M2; ANE-eligible in oMLX 0.6.1 (+21% prefill measured on M2 Ultra)
• Qwen3.8-27B-oQ6e-mtp — 6-bit / bf16, ~22 GB, for 64 GB-class machines
• Qwen3.8-27B-oQ8e-fp16-mtp — 8-bit / fp16, ~27.5 GiB, highest fidelity

Quantization details

• Model type: qwen3_5
• Bits: 4 (effective ~4.9 bpw mixed precision), group size 64 — ~17 GB weights
• Enhanced quantization (oQe): imatrix-calibrated (1024 samples) — affine quantization weighted by activation importance
• MTP weights preserved (mtp.* tensors + config) — multi-token-prediction / Lightning MTP works after quantization
• Non-quant weight dtype: bfloat16 (matches the base model; the safe choice on M3/M4/M5)
• Vision components included (not a text-only strip)
• Format: MLX safetensors

Recommended sampling (per the Qwen3.8 model card)

Mode	temperature	top_p
Thinking (default)	1.0	0.95
Instruct (non-thinking)	0.7	0.80

Thinking controls via chat_template_kwargs:

• enable_thinking (default true)
• preserve_thinking (default true) — keeps reasoning traces across multi-turn history
• reasoning_effort: xhigh (default) / medium / low — in our testing (on the 8-bit sibling), medium reduced thinking volume ~25% with no loss on agentic tasks

Measured context windows (64 GB M5 Pro, oMLX, default wired-memory limit)

Nonce-cold ladder, 2026-08-14: 64K OK (207s cold prefill), 131K OK (457s), 160K crashed the oMLX server (allocation spike during prefill; the server did not gracefully reject). Bisect found ~152K safe; 131,072 is the recommended client window on 64 GB machines, leaving margin below the crash edge.

Two practical warnings: (1) real windows on this hybrid architecture are prefill-overhead-bound, not KV-bound — KV itself is cheap (only 16 of 64 layers are full attention, ~64 KB/token in bf16), but allocation peaks during long prefill run well above weights+KV, so don't size your window from KV math; (2) cap the context client-side — an over-window request may crash the server rather than return an error. The full 262,144 native window was validated with the 8-bit sibling on a Mac Studio.

ANE prompt processing note (oMLX 0.6.2, experimental)

This q4 layout is eligible for oMLX's experimental ANE/GPU prefill split. The 0.6.2 built-in tuner, run on a 64 GB M5 Pro with this build, measured a best case of +9.2% (498.6 prompt tok/s) at MLP 35% / GDN 45% — modest, because single-die chips have one ANE (the dual-ANE M2 Ultra measured +37.6% at the same tuner shape with the fp16 sibling; see its card for full measurements). On 64 GB machines we recommend leaving it off anyway: the ANE path keeps ~4 GB extra resident, which comes out of exactly the headroom behind the 131,072 recommended window — and an over-margin prefill hard-crashes the server rather than returning an error. If you enable it, re-run a context ladder before trusting any large window. Real-world gains are also below the tuner figure, which uses an ideally block-aligned 4,097-token shape.

Fidelity note

4-bit is the aggressive end for a 27B reasoning model; the imatrix calibration mitigates but does not eliminate low-bit degradation, so expect the largest quality gap of the family on long reasoning chains. That said, this exact build passed the same multi-turn agentic battery as the 8-bit sibling (2026-08-14, M2 Mac Studio): 3/3 scenarios completed with correct answers, 0 stalled turns across 8 agentic tool-call turns with thinking fed back into history, and thinking volume comparable to the 8-bit (~80% of its reasoning chars). The battery's scenarios are short; the caveat about very long reasoning chains stands. If your machine fits the 6-bit at your target context length, prefer it.