Qwen3.8-Flash-Next-VQ-4.4bpw

93.6 GiB — a 335 GiB frontier MoE on 128 GB machines.

A data-free vector-quantized build of Qwen3.8-Flash-Next (180B total / 10-of-512 active, 51.2B n-gram PLE, vision) for Apple Silicon. Stock mlx-lm, no patches — the VQ runtime ships inside the checkpoint as model.py. Built with VQLab.

MoE experts at d=2/K=256, PLE at d=2/K=256, and seven layers (0, 1, 2, 16, 24, 31, 39) at d=2/K=1024 (10-bit packed) experts — the set chosen by KL for THIS rung; the 3.2's set does not transfer (v2, see the changelog).

The affine builds compared against below are our own conversions made with the same tooling, scored on the same instrument.

Requirements

Requires Knurlogic 0.1.3 or newer. Earlier Knurlogic releases, and plain mlx-lm builds of qwen4_exp that rebuild the PLE hash multipliers from the default seed, hash tokens into the n-gram embedding tables with the wrong multipliers and read the wrong rows on every token. The model still loads and generates, but its output is degraded.

Under Knurlogic none of this section applies: Knurlogic registers the qwen4_exp architecture itself (see Run it). The rest of this section is for plain mlx-lm.

This model needs an mlx-lm that has the qwen4_exp architecture, which no released version has yet. The architecture is in ml-explore/mlx-lm PR #1788, still unmerged as of 2026-09-09. On a stock pip install mlx-lm you will get:

ModuleNotFoundError: No module named 'mlx_lm.models.qwen4_exp'

That is the architecture missing, not a problem with this artifact. Until the PR merges:

pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1788/head

The VQ runtime itself needs no patches — it ships inside the checkpoint as model.py and stock mlx-lm executes it. The PR is required only for the base architecture, and only without Knurlogic.

Two or more Macs: Knurlogic splits one model across two or more Macs. Install the same knurlogic version and the model on each, run knurlogic ui --host cluster on each, and launch the model from the page with the machines selected. Knurlogic drafts with mtp-head-q6.safetensors when the model has it (see Speculative decoding). Budget ≈2.2 GiB extra resident when drafting is enabled. Throughput through exo, the earlier serving path, was measured only on the 2.1bpw rung (23.7–25.5 tok/s, acceptance 0.82–0.88); the sidecar head is the same file on every rung.

Changelog

2026-10-04 — rescored on Knurlogic 0.1.3

Knurlogic 0.1.3 fixes how Qwen3.8-Flash-Next hashes tokens into its per-layer n-gram embedding (PLE) tables: earlier runtimes rebuilt the hash multipliers from a wrong default seed instead of using the values stored in the checkpoint, so every build of this model (and Qwen's own weights) read the wrong embedding rows on every token. This model now requires Knurlogic 0.1.3 or newer for correct output. The weights and the bundled model.py are unchanged; every number in Measured results was re-measured on the fixed runtime.

2026-10-01 — runtime refresh; Knurlogic

The bundled model.py is updated to the current VQ runtime, and the v2 weights described in the 2026-09-17 entry are published with this revision for the first time. The run, multi-Mac, drafting and vision instructions now use Knurlogic.

2026-09-17 — v2 codebooks: per-layer allocation chosen by KL

Weights changed. L35 and L36 demoted d2-K1024 → d2-K256, L2, L16 and L24 promoted d2-K256 → d2-K1024 (+326 MB, 0.33%). Size 94.4 GiB. The codebooks are the same data-free k-means fits; only WHICH layers carry the wider tier changed, and it was chosen by the same leave-one-out + 42-layer add-one sweep as the 3.2, run on this rung's own codebooks: the best layer here (L2) is a KL loser on the 3.2, and the 3.2's best (L5) is the worst here (F119). L24 was added to hold literary flat.

KL to the bf16 teacher, millinats per token, paired on identical positions, three house corpora at 12288 tokens (|t| > 2 is a real difference):

Measured on the runtime before Knurlogic 0.1.3; not comparable with Measured results below.

Perplexity on the same runs, printed with its sign and NOT used as a gate (it disagrees with KL in sign on prose for this family — F116–F118): prose 5.0583 → 5.2045, code 1.6292 → 1.6315, literary 6.5232 → 6.5297.

Runtime: this bundle carries the v2 runtime profile (bf16 I/O on; F53/F105), +11% prefill / +19% decode over the arc6 runtime published 2026-09-09.

2026-09-09 — runtime refresh

Runtime refresh. The bundled model.py is updated so downloaders run exactly the code that was benchmarked; dense bundles now carry both runtimes.

• Faster prefill on affected geometries, measured per rung: a device-codebook kernel arm for large-codebook geometries (up to 1.46x on affected rungs), a ragged-subvector relaxation (up to 1.34x on affected rungs), a fused d8 arm, and a routing memo (≈2%). No blanket speedup is claimed across the lineup — gains apply only where the geometry engages the new paths.
• Output quality is unchanged: the kernel changes are bit-identical or 1-ULP-equivalent, and the routing memo is bit-identical (logits checksum verified).
• Speculative decoding (MTP): on repos that ship mtp-head-q6.safetensors, the sidecar works with the exo fork branch mtp-stage1 (github.com/noahzelezny/exo) — launch each node with exo --mtp (or set EXO_MTP=1). Note: with MTP enabled, exo serves requests sequentially (the batch engine has no MTP path), so leave it off for concurrent / multi-agent workloads.

2026-09 — bundle refresh

Bundle refresh.

model.py is updated:

• Faster MoE expert kernels (threadgroup-occupancy fix, device-direct activation reads, template-free dispatch, and a simd_sum reduction). Measured on this family: decode 17.4 -> 18.8 tok/s (+8%, A-B-A, M3 Ultra). All kernels verified bit-identical to the previous bundle except the reduction, which is 1-ULP equivalent with a measured quality delta of exactly zero (identical perplexity to 16 digits, KL 0.0 between old and new logits). VQ_D8_SS=0 restores the bit-identical legacy reduction.
• The runtime caps MLX's buffer-reuse cache by default (VQLAB_CACHE_LIMIT_GB overrides, 0 disables), so long-prompt prefill no longer balloons transient memory — peak stays near resident instead of a multiple of it.
• The bundle loads under both mlx-lm and mlx_vlm.
• Nothing you have downloaded breaks: weights unchanged, only model.py and config.json keys replaced; prior revisions stay pinnable by commit hash.

Memory, measured externally (process RSS sampled at 5 Hz from outside, M4 128 GB, weights ≈94 GiB on disk):

• ≈36 GiB sustained across varied short-context chat (8 prompts x 200 tokens; flat — expert routing concentrates, and untouched expert pages are never loaded).
• 58.5 GiB peak for load + 2048-token prefill + 128-token decode.
• ≈94 GiB (the weight size) is the only figure that covers every possible routing pattern; budget it for worst-case or adversarially varied traffic.

Transient prefill allocations are returned to the OS as they free, so peak tracks what the routing touches rather than a multiple of it. Add ≈2.2 GiB when the MTP sidecar is enabled.

Measured results

Every row re-measured 2026-10-04 on Knurlogic 0.1.3 (mlx 0.32.3). KL in millinats per token against the bf16 teacher's cached top-64 logits, 12288 tokens per corpus on three corpora (prose, code, literary); every row is scored on the same cache and the same positions. Sizes are text weights; the VQ builds also carry a 0.84 GiB vision tower, and every build ships a 2.1 GiB MTP head. Numbers published before 2026-10-04 were measured on runtimes that hashed the PLE tables wrongly (see the changelog) and are not comparable to these. KL is higher than in those earlier tables because the fixed teacher is much sharper (prose perplexity 2.31); the ordering of builds is unchanged.

Paired at matched size. Differences are VQ minus affine in KL, with t computed on identical positions (|t| > 2 is a real difference):

• against affine q4, 2 GiB larger: prose -84% (t -47.3), code -83% (t -27.3), literary -87% (t -19.8);
• against affine q5, 23 GiB larger: prose -44% (t -17.2), code -67% (t -15.7), literary -48% (t -6.7).

Rank by KL, not perplexity. KL and top-1 order this ladder as the bit budgets predict; perplexity does not. VQ-3.2bpw reads 2.300 on prose, lower than VQ-4.4bpw (2.323) and affine q8 (2.367), while sitting at six times q8's KL. Perplexity is a mean over finite text and absorbs offsetting errors; KL measures the distribution itself.

Speed

Measured 2026-10-01 on this revision.

Split across two Macs with Knurlogic (M3 Ultra 96 GB + M4 Max 128 GB, pipeline over Thunderbolt), against Qwen3.8-Flash-Next-8bit on the same two Macs: 3 pairs, a prompt of about 1,900 tokens, 128 generated tokens, no drafting.

A two-Mac split's absolute speed depends on the link, so only the ratio is quoted.

Run it

Requires a Mac with Apple Silicon (macOS, MLX).

With Knurlogic, which works out the settings and checks the model fits before loading it:

pip install "knurlogic>=0.1.3"
hf download TheDrainFlorist/Qwen3.8-Flash-Next-VQ-4.4bpw --local-dir ~/Knurlogic/Models/Qwen3.8-Flash-Next-VQ-4.4bpw
knurlogic serve ~/Knurlogic/Models/Qwen3.8-Flash-Next-VQ-4.4bpw

Chat at http://127.0.0.1:8080/, or point any OpenAI-, Anthropic- or Ollama-compatible client at it (model local). knurlogic ui opens a page with a Launch button for every model on disk instead.

At 104 GB on disk this needs a Mac with more memory than that, or two Macs: install the same Knurlogic version and the model on each, run knurlogic ui --host cluster on both, and launch it from the page with both machines selected. Knurlogic splits the layers between them.

Images work through Knurlogic directly; mlx-vlm is not needed.

The VQ runtime ships inside this repo as model.py (declared by model_file in config.json), and Knurlogic runs the file the model ships.

Without Knurlogic:

pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1788/head

python -m mlx_lm generate \
  --model TheDrainFlorist/Qwen3.8-Flash-Next-VQ-4.4bpw \
  --prompt "Explain vector quantization briefly." \
  --max-tokens 512

Speculative decoding (MTP)

This repo includes mtp-head-q6.safetensors (2.1 GiB): the model's own multi-token-prediction head, quantized. Stock loaders ignore it — it costs nothing on disk-to-RAM unless you opt in by name.

When enabled it adds ≈2.2 GiB resident (head weights + its cache) on top of the trunk, so budget for it. What you get, measured on this artifact (M3 Ultra, greedy, 378-token runs): 18.1 -> 24.4 tok/s, acceptance 0.77. The trunk verifies every drafted token by exact rejection sampling, so the output distribution is exactly the base model's. Prefill in the speculative path is chunked (2048-token chunks, VQLAB_PREFILL_CHUNK overrides) with per-chunk eval, so long prompts stay within the same memory envelope as plain decoding.

Knurlogic loads the head and drafts automatically (its lean preset turns MTP off). To serve it with VQLab instead:

git clone https://github.com/noahzelezny/VQLab && cd VQLab
python3 -m venv .venv && source .venv/bin/activate
pip install .
python -m vqlab.cli serve \
  --model TheDrainFlorist/Qwen3.8-Flash-Next-VQ-4.4bpw \
  --sidecar mtp-head-q6.safetensors

OpenAI-compatible API on localhost; vqlab mtp-generate for one-shot CLI use. Without --sidecar, nothing about the model changes.

Methodology

Fitted data-free from the bf16 checkpoint — k-means / Lloyd over weight subvectors, seed 1234, no Hessian, no activation statistics, no calibration corpus. Recipes are in the VQLab repo.

Quantization damage is not uniform across layers. A one-pass probe (teacher and student streamed together, per-layer local damage measured with no compounding) shows the same hot set on every rung of this family: layer 1 dominates, a late band (31–39) follows, and the map is identical across geometries (rank correlation 0.905, identical top-10). Upgrading only those layers buys 15–24% KL for 3–4% size on the lower rungs; the probe, the mixing, and the verdicts are all reproducible with VQLab (vqlab layer-leverage, scatter fits via fit-moe --vq-layers).

Verification

Release gates passed on this artifact before upload: file, index and tokenizer checks, a verbatim match between the bundled runtime and its source, and a generation smoke through the shipping runtime on Apple Silicon. The upload path runs the gate itself and refuses to publish without it.

Limitations

• Requires Knurlogic 0.1.3 or newer. Older runtimes, and plain mlx-lm builds that rebuild the PLE hash multipliers from the default seed, read the wrong n-gram embedding rows. See Requirements.
• Plain mlx-lm needs an unreleased version. The qwen4_exp architecture is in PR #1788, still unmerged as of 2026-09-09. Knurlogic does not need it. See Requirements.
• Through-exo throughput is measured on the 2.1bpw rung only (23.7–25.5 tok/s, acceptance 0.82–0.88). The sidecar head is the same file on every rung, but this rung's cluster numbers are not measured.
• Rank the table by KL, not perplexity. Perplexity is an aggregate over finite text and absorbs offsetting errors; several rungs read within noise of the teacher on perplexity while differing by an order of magnitude in KL.
• The affine comparators are our own conversions, not community builds.
• Perplexity is not comparable across model families — only within this table, which is one instrument on one corpus set.

Paper

The method, the full model ladder, the negative results, and the measurement rules behind every number here: Data-Free Vector Quantization Beats Affine Quantization at Matched Bytes Below 6 Bits (CC BY 4.0) · code: VQLab · web version: Space

Support this work

VQLab and these artifacts are built and released independently — the fits, the measurement harness and the published rungs are one person's compute and time. If they are useful to you:

• Sponsor: github.com/sponsors/noahzelezny
• Contract work: available for quantization and on-device inference work on Apple silicon — custom rungs, per-layer allocation for your model, or getting a checkpoint to run well on a Mac. Contact: hello@thedrainflorist.com

Provenance

Base model: Qwen/Qwen3.8-Flash-Next — released under the Qwen Community License 1.0. This is a quantized derivative and inherits that license; using it means accepting the base model's terms. Quantization: TheDrainFlorist, 2026.

Local artifact: qwen4exp_vq_packed_92mix6.

Built with MLX and VQLab.