Qwen3.8-27B-oQ4e-fp16-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 fp16 variant of the 4-bit build, tuned for M1/M2-generation Apple Silicon, where fp16 non-quant weights prefill ~20% faster than bf16. It is also the family's ANE-eligible speed build: oMLX 0.6.1's experimental Qwen ANE prompt processing accepts this q4 layout, measured at +21% prefill throughput on an M2 Ultra (details below). Made with oQ (oMLX v0.6.1) mixed-precision quantization.

Sibling repos:

• Qwen3.8-27B-oQ4e-mtp — 4-bit / bf16, the same weights in the safe dtype for M3/M4/M5 machines
• 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 — ~16.7 GiB weights
• Enhanced quantization (oQe): imatrix-calibrated (1024 samples, shared calibration with the family) — affine quantization weighted by activation importance
• MTP weights preserved (mtp.* tensors + config) — multi-token-prediction / Lightning MTP works after quantization
• Non-quant weight dtype: float16 — ~20% faster prefill on M1/M2; on M3+ prefer the bf16 sibling. No fp16 activation-overflow issues observed with Qwen3.8 in extended agentic use (this is a Gemma-class problem, not a Qwen one)
• 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: ANE prompt processing (oMLX 0.6.1, experimental)

This layout (q4, group size 64) is eligible for oMLX 0.6.1's dual-ANE/GPU prefill split. Measured on an M2 Ultra Mac Studio (128 GB), ~78K-token cold prefills, 3 unique-prompt runs per leg, prefix cache verified cold:

Configuration	Prefill tok/s
GPU only (ANE off)	~222
ANE on (dual-ANE + GPU)	~269 (+21%)
8-bit sibling, GPU only (ANE-ineligible), same machine	~207†

†Measured at ~88K, depth-adjusted to ~78K. The +21% slightly exceeds the M3 Ultra reference figures (+18.9% at 32K) because longer prompts amortize more complete 2,048-token ANE blocks — the win grows with prompt length and applies to cold prefills only (prefix-cached turns are unaffected).

oMLX 0.6.2's built-in split tuner, swept on the same M2 Ultra, confirmed 53% MLP / 50% GDN as the optimal split and measured +37.6% (444.9 prompt tok/s) at its shorter benchmark prompt shape. The percentage gain varies with prompt depth: it rises with block count out of the small-prompt regime, peaks mid-range, then tapers at very long contexts as the full-attention layers (not ANE-accelerated) claim a growing share of prefill time — hence +37.6% at the tuner's shape vs. +21% at 78K.

Costs to know before enabling: ~4.15 GB extra resident memory for the model's lifetime (≈68K tokens' worth of KV cache), ~40 s eager ANE program compile added to every model load, and an approximate INT8 weight copy on the ANE path (upstream reports matching output hashes at 16K/32K vs. the GPU path). On 64 GB machines, leave it off — the resident cost comes out of exactly the headroom that long-context prefill needs.

Measured context windows

The 64 GB ladder was run on the bf16 sibling (M5 Pro, oMLX, default wired-memory limit): 64K OK, 131K OK, 160K crashed the oMLX server (allocation spike during prefill; no graceful rejection) — 131,072 is the recommended client window on 64 GB machines. Real windows on this hybrid architecture are prefill-overhead-bound, not KV-bound, so don't size your window from KV math, and 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 128 GB Mac Studio.

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. This exact build passed the family's multi-turn agentic battery (2026-08-18, M2 Mac Studio, thinking fed back into history verbatim): 3/3 scenarios completed with correct answers, 0 stalled turns across 8 agentic tool-call turns. The battery's scenarios are short; the caveat about very long reasoning chains stands. A good deployment pattern from our own use: run this build as a fast long-context instruct tier (batch summarization/extraction, one-shot long-document Q&A — cold-prefill-dominated work where the ANE gain lands and quant sensitivity is lowest) and keep an 8-bit sibling for thinking-grade tasks.