[!IMPORTANT] Superseded by Qwen3.8 — use this instead: AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED

Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX): AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED

This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports.

Serve with the unified container: ghcr.io/aeon-7/aeon-vllm-ultimate:latest (Spark / GB10) or ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest (RTX discrete).

Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS

💎 Want higher quality? This is the XS (smallest-footprint) body. If you have the VRAM — it's only slightly larger — the full Multimodal-NVFP4-MTP body runs at parity speed with materially higher quality-eval scores, and is the recommended DGX Spark daily-driver. Stay on XS when footprint / VRAM is tight.

Deployment, operations & benchmarks → github.com/AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-DFlash

The GitHub repo is the source of truth for the production deployment guide, hardware-tuned docker-compose configs, full configuration reference, measured benchmarks, and AGENTS.md — an operator's manual that pre-empts common stale-documentation traps.

🆕 Canonical container — AEON vLLM Ultimate

ghcr.io/aeon-7/aeon-vllm-ultimate:latest (= tag :2026-06-18-v0.23.0-dflashfix; rollback tag :2026-06-11-pr41703) — vLLM 0.23.0 built from source for sm_121a + DFlash high-concurrency fix (drafter block-table slice; scales cleanly to c=64) + PR #40898 (DFlash sliding-window attention) + PR #41703 (prefix-caching corruption-immunity with DFlash) + PR #44389 Triton NVFP4 KV cache (~3× KV capacity) + DFlash + TurboQuant K8V4 + AEON sm_121a patches. This is the single image for all Qwen3.6-27B AEON-Ultimate repos.

The image ENTRYPOINT is /bin/bash, so a docker run must use --entrypoint vllm and then serve … — do not write IMAGE vllm serve (that runs bash vllm serve and fails).

The XS body — even though it ships with native MTP heads — works great with an external DFlash drafter. Pair with z-lab/Qwen3.6-27B-DFlash for big throughput gains over the native MTP path.

🚀 Quickstart (DGX Spark / GB10 — DFlash)

Complete copy-paste recipe: pull the container, pull this model, pull the DFlash drafter, then serve. (Fuller deployment options — dedicated-VRAM Blackwell MTP, env vars, compose — are in the Usage section below.)

# 1) Pull the canonical AEON vLLM Ultimate container
docker pull ghcr.io/aeon-7/aeon-vllm-ultimate:latest

# 2) Pull THIS model (fresh)
huggingface-cli download AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS --local-dir ./aeon-model

# 3) Pull the DFlash drafter (fresh)
huggingface-cli download z-lab/Qwen3.6-27B-DFlash --local-dir ./aeon-drafter

# 4) Serve (ENTRYPOINT is /bin/bash, so pass --entrypoint vllm then serve …)
docker run --rm --gpus all \
  -v ./aeon-model:/model:ro \
  -v ./aeon-drafter:/drafter:ro \
  --entrypoint vllm ghcr.io/aeon-7/aeon-vllm-ultimate:latest \
  serve /model \
  --quantization modelopt \
  --mamba-cache-dtype float32 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --enable-auto-tool-choice \
  --limit-mm-per-prompt '{"image":4,"video":2}' \
  --mm-encoder-tp-mode data \
  --gpu-memory-utilization 0.65 \
  --max-num-seqs 64 \
  --max-num-batched-tokens 16384 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --trust-remote-code \
  --speculative-config '{"method":"dflash","model":"/drafter","num_speculative_tokens":10}'

Lower --gpu-memory-utilization (e.g. 0.69) if the host co-runs other services; on DGX Spark unified memory keep it at 0.6-0.7 — above ~0.8 the shared CPU+GPU pool page-thrashes and stalls the box (even 0.85 stalls). For dedicated-VRAM Blackwell (MTP via the grafted head, no external drafter) see Usage.

📈 Why this image matters for long-context drafting

The z-lab Qwen3.6-27B DFlash drafter is a sliding-window model — 4 of its 5 layers use sliding-window attention (window 2048). aeon-vllm-ultimate:latest (PR #40898) runs those layers as proper SWA; earlier images ran them as full attention, so drafting collapsed once context grew past ~2048 tokens. PR #41703 additionally makes --enable-prefix-caching corruption-immune with DFlash. Net: long-context drafting holds up; short-context (<2048, one window) is unchanged.

🏆 Live production bench — DFlash n=10 on aeon-vllm-ultimate:latest

Measured on DGX Spark GB10, aeon-vllm-ultimate:latest, DFlash num_speculative_tokens=10. Lead with acceptance (stable across samples), not single-sample tok/s.

Long-context (~9k-token) draft acceptance — this is the headline win:

Image	~9k-token draft acceptance
pre-fix image (full-attn drafter)	19.7 %
aeon-vllm-ultimate:latest (SWA drafter)	45.0 % (2.3×)

Short-context c=1 acceptance by category (new image, n=10, approximate):

Category	accept
Math	~50 %
Reasoning	~50 %
Extraction	~40 %
Coding	~38 %
Natural	~25 %
Prose	~18 %

Short-context throughput is statistically unchanged vs the prior image — the drafter's sliding window only engages past 2048 tokens, so the win is specifically long-context. (Caveat: single / 3-round samples; short-context rankings are within noise. Acceptance is the stable signal — single-sample tok/s is not.)

🙏 Reference recipe credit: The conv1d-preserved NVFP4 + MTP graft pipeline used to build this XS variant is based on sakamakismile's validated Qwen3.6-27B-NVFP4-MTP series (22K+ downloads). They worked out the modelopt config — including the strategic decision to quantize the GDN projection matmuls to NVFP4 while preserving linear_attn.conv1d at BF16 — and the MTP-head graft technique. We adapted the recipe to AEON-Ultimate's abliterated weights and ship both the conv1d-preserved-only XS variant (matching their footprint) and a heavier regular-MTP variant that additionally keeps the projections at BF16. Full credit for the underlying recipe → sakamakismile.

Performance — DGX Spark (v0.23.0, aeon-vllm-ultimate:latest)

Fastest 27B export. NVFP4 MTP-XS body + an external DFlash@10 drafter on ghcr.io/aeon-7/aeon-vllm-ultimate:latest (vLLM 0.23.0) delivers ~42.6 tok/s single-stream and ~340 tok/s aggregate at c=64, with DFlash draft acceptance ~35 % at short context that holds ~45 % at long (~9k) context. This is the body the canonical 27B card was benchmarked on.

Measured on DGX Spark GB10 (sm_121a, unified memory), aeon-vllm-ultimate:latest, NVFP4 body served with an external DFlash drafter at num_speculative_tokens=10. The grafted MTP head ships in this repo but sits unused on the Spark — DFlash wins on unified memory (see hardware routing below).

MTP-XS is the smallest NVFP4 export and the fastest single-stream of the 27B family, at roughly half the memory of the BF16 baseline.

Per-category single-stream (c=1)

Decode speed tracks DFlash acceptance: structured workloads (Extraction, Math, Reasoning) draft well (≈42–49 % accept → 49–57 tok/s); free-form prose drafts less predictably (≈23 % → 31 tok/s). The headline ~42.6 tok/s is the Coding-category single-stream figure.

Aggregate throughput by concurrency

Throughput scales cleanly to c=64 (the DFlash high-concurrency fix below removed the prior c≥32 crash). Aggregate peaks at c=64, topping out around ~340 tok/s (Reasoning category); every category climbs monotonically from c=1 → c=64:

Long-context DFlash acceptance

The z-lab DFlash drafter is a sliding-window model (4 of 5 layers use SWA, window 2048). On this image (PR #40898) those layers run as proper SWA, so draft acceptance holds as context grows instead of collapsing past 2k tokens:

This is the headline long-context win — acceptance at ~9k tokens is higher than the blended short-context average. (Pre-fix full-attention image collapsed to ~19.7 % at the same context.)

What we fixed for the DGX Spark

All AEON models run on one unified container — ghcr.io/aeon-7/aeon-vllm-ultimate:latest (vLLM 0.23.0 built from source for sm_121a, merged with the AEON speculative-decoding stack). Two fixes matter most for this card:

• Unified container. A single sm_121a image (vLLM 0.23.0) replaces the per-model image sprawl — the same build serves every Qwen3.6-27B AEON-Ultimate variant, with the SM120-family CUTLASS NVFP4/FP8 kernels GB10 actually dispatches to.
• DFlash high-concurrency fix. The speculative drafter previously crashed at ≥32 concurrent requests (a padded-vs-unpadded KV block-table shape mismatch in FlashAttention). The fix slices the drafter's block-table to the unpadded batch (block_table[:num_reqs]) — a port of upstream PR #43982, which fixed this for MTP but never for DFlash. The c=32 / c=64 columns above are only measurable because of it.

Full optimization writeup (NVFP4 KV cache, DFlash SWA, sm_121a build, unified-memory tuning): see the container repo.

Stock baseline pending. These figures are on the optimized aeon-vllm-ultimate:latest. There is no stock / vanilla-vLLM baseline for this export yet — a fresh fully-vanilla re-bench (default settings, no speculative decoding, no sm_121a optimizations) is pending and will be added when it completes.

What "XS" means — and what it's not

This is the extra-small footprint sibling of -Multimodal-NVFP4-MTP. XS is not "everything to FP4." It is a deliberate, principled split: the heavy GDN matmul projections drop to NVFP4 (where they're bandwidth-bound and FP4 wins big), while the SSM-critical linear_attn.conv1d kernel stays BF16 (where FP4 has documented stability problems on long-context recurrence).

This is a smart, strategic quantization — not a precision compromise. The conv1d preservation matters: the GatedDeltaNet recurrence depends on the 1D convolution behaving numerically like its training distribution, and FP4 quantization of conv1d has been observed to cause drift on long-context inference in community testing. By keeping conv1d BF16 while quantizing the projections (which are bandwidth-limited matmuls where FP4 is a clean win), we get the ~6 GB footprint reduction without sacrificing the part of the model that's actually fragile under quantization. This is the same principle modelopt's NVFP4_DEFAULT_CFG applies by default and the same recipe sakamakismile validated across his Qwen3.6-NVFP4-MTP series (22K+ downloads).

When to pick which:

• Pick the regular variant if you have ≥48 GB VRAM. Even the projection weights at BF16 give a small additional safety margin on long-context recurrence stability.
• Pick this XS variant if you have 24–32 GB VRAM (RTX 5090, single GPUs without headroom for full BF16 GDN). The conv1d preservation guarantees the SSM recurrence stays numerically stable; the ~6 GB savings buy meaningful KV-cache headroom on tight GPUs.

We ship both because we have the headroom on RTX PRO 6000 / B100/B200 to run the larger, more numerically-conservative version, and several users on tighter cards have asked for the smaller one. Neither variant quantizes linear_attn.conv1d — that would be a different (and not-recommended) variant we have explicitly chosen not to ship.

Variants

What this is

The modelopt-format NVFP4 + MTP variant, multimodal-preserved, with linear_attn projections fully quantized, of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 — the lossless abliteration of Qwen 3.6 27B (KL 0.000492 vs base, 0/100 refusals, multimodal preserved, hybrid GDN-aware quantization).

Specifically:

• Body quantized to NVFP4 via nvidia-modelopt 0.43.0 with NVFP4_DEFAULT_CFG. modelopt format, served by vLLM through --quantization modelopt.
• Linear-attn / GatedDeltaNet projections quantized to NVFP4 (this is the XS difference). Only linear_attn.conv1d is kept BF16 (modelopt's default). The community has validated this approach on Qwen3.5/3.6-NVFP4 builds with 22K+ downloads on sakamakismile's reference recipes; we re-ran calibration on our abliterated weights and the model serves correctly.
• Vision tower preserved BF16 (333 keys) — correct model.visual.* layout. Multimodal weights load; runtime vision inference validation on this image is pending a GPU window.
• MTP head grafted from the base Qwen/Qwen3.6-27B checkpoint (15 tensors, BF16, bit-exact verified). Powers --speculative-config '{"method":"qwen3_5_mtp",...}' for self-speculative decoding without a separate drafter.

Why MTP

Multi-Token Prediction (MTP) lets the model predict multiple future tokens per forward pass via the trained mtp.* head, enabling speculative decoding without a separate drafter model. The acceptance rate is high because the drafter is the model itself — same architecture, same weights, same distribution.

Indicative published numbers (sakamakismile's reference recipe on RTX 5090):

• Single-stream short prompts at n=3: ~132 tok/s
• Single-stream long-form: ~105 tok/s
• 2-parallel aggregate (256K + KV FP8): ~189-207 tok/s
• Mean acceptance length: ~3.0-4.0 (compared to DFlash chains of ~2.0-2.3)

Validated benchmarks of the AEON-Ultimate XS variant land in the GitHub repo once measured.

🎯 When to pick this variant — measured hardware routing

The right speculative-decode method depends on memory architecture:

Full bench numbers: GitHub repo Performance section.

Usage

vLLM serve — dedicated-VRAM Blackwell (default: MTP via grafted head)

# One-time: pull this repo locally
hf download AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-Multimodal-NVFP4-MTP-XS \
  --local-dir ./aeon-ultimate-multimodal-nvfp4-mtp-xs

# Serve
export VLLM_NVFP4_GEMM_BACKEND=flashinfer-cutlass
export VLLM_USE_FLASHINFER_MOE_FP4=0
export VLLM_USE_FLASHINFER_SAMPLER=1

vllm serve ./aeon-ultimate-multimodal-nvfp4-mtp-xs \
  --quantization modelopt \
  --trust-remote-code \
  --limit-mm-per-prompt '{"image":4,"video":2}' \
  --mm-encoder-tp-mode data \
  --max-model-len 262144 \
  --max-num-seqs 32 \
  --max-num-batched-tokens 16384 \
  --gpu-memory-utilization 0.70 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --enable-auto-tool-choice \
  --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}'

num_speculative_tokens=3 is the canonical setting for qwen3_5_mtp. Higher values diverge the drafter further from the target distribution and acceptance falls.

vLLM serve — DGX Spark (DFlash spec, not MTP — current production recipe)

For DGX Spark, swap the spec method to DFlash. DFlash's block-diffusion drafter is decisively better than MTP's n=3 on unified memory. This is the exact recipe running in production, on the AEON vLLM Ultimate image ghcr.io/aeon-7/aeon-vllm-ultimate:latest (vLLM 0.23.0 + DFlash high-concurrency fix + PR #40898 + PR #41703 + PR #44389).

The image's ENTRYPOINT is /bin/bash, so when launching via docker run you must pass --entrypoint vllm and then serve … (writing IMAGE vllm serve runs bash vllm serve and fails). The vllm serve … arguments are identical either way:

# Pull the DFlash drafter alongside this body
hf download z-lab/Qwen3.6-27B-DFlash --local-dir ./qwen36-27b-dflash

export VLLM_NVFP4_GEMM_BACKEND=flashinfer-cutlass
export VLLM_USE_FLASHINFER_MOE_FP4=0
export VLLM_USE_FLASHINFER_SAMPLER=1

# docker run --rm --gpus all --entrypoint vllm \
#   -v "$PWD":/models ghcr.io/aeon-7/aeon-vllm-ultimate:latest \
#   serve /models/aeon-ultimate-multimodal-nvfp4-mtp-xs \
#   ... (the same flags below) ...

vllm serve ./aeon-ultimate-multimodal-nvfp4-mtp-xs \
  --quantization modelopt \
  --trust-remote-code \
  --mamba-cache-dtype float32 \
  --limit-mm-per-prompt '{"image":4,"video":2}' \
  --mm-encoder-tp-mode data \
  --max-model-len 262144 \
  --max-num-seqs 64 \
  --max-num-batched-tokens 16384 \
  --gpu-memory-utilization 0.69 \
  --enable-chunked-prefill \
  --enable-prefix-caching \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --enable-auto-tool-choice \
  --speculative-config '{"method":"dflash","model":"./qwen36-27b-dflash","num_speculative_tokens":10}'

Critical DFlash config rules (learned the hard way):

• Use the DEFAULT drafter attention backend — do not add an attention_backend to the spec-config. The default works for Qwen3.6 DFlash on this image (unlike Gemma's DFlash, which needed an explicit flash_attn backend). Leave it out.
• Do NOT set --kv-cache-dtype. DFlash's drafter is non-causal (block diffusion) and no vLLM backend supports non-causal + fp8 KV, so KV must stay at default BF16. Forcing fp8 KV will fail to boot.
• num_speculative_tokens=10 is the validated default. An n=8/10/12/15 sweep found n=10 best for the Spark voice/chat workload — top aggregate throughput + DFlash acceptance at parity single-stream; the old 12-token setting wins only at very long context (z-lab's published default is 15).
• --gpu-memory-utilization 0.69 because this host co-runs Qwen3-ASR (:8001) + Qwen3-TTS (:8002). Keep it ≤ 0.7 when co-hosting; even with vLLM running alone stay at 0.6-0.7 on the DGX Spark's unified memory — above ~0.8 the shared CPU+GPU pool page-thrashes and stalls the box (even 0.85 stalls). BF16 KV is 2× fp8, but full 256k context still fits — KV cache holds 487k tokens / 1.86× concurrency at 262,144 ctx.

Why this recipe needs aeon-vllm-ultimate:latest: the z-lab DFlash drafter is a sliding-window model — 4 of its 5 layers use sliding-window attention (window 2048). This image (PR #40898) runs those layers as proper SWA; earlier images ran them as full attention, so drafting collapsed once context grew past ~2048 tokens. PR #41703 additionally makes --enable-prefix-caching corruption-immune with DFlash. Net: long-context drafting holds up; short-context (<2048, one window) is unchanged. See the live acceptance bench at the top (45.0 % @ ~9k vs 19.7 % pre-fix = 2.3×).

Configuration notes

• --quantization modelopt is required for this body (not compressed-tensors — different format).
• --speculative-config '{"method":"qwen3_5_mtp", ...}' uses the grafted MTP head; correct for dedicated-VRAM Blackwell. Don't use this on DGX Spark.
• --speculative-config '{"method":"dflash", ...}' uses an external DFlash drafter; correct for DGX Spark. The grafted MTP head in this repo sits unused in this path (~0.85 GB dead weight). Don't use this on RTX PRO 6000 or B100/B200 — they prefer MTP.
• --gpu-memory-utilization 0.94 is the validated cap on RTX PRO 6000; on DGX Spark keep it at 0.6-0.7 (unified memory page-thrashes above ~0.8 — even 0.85 stalls).

Quantization recipe

• Tool: nvidia-modelopt 0.43.0 with NVFP4_DEFAULT_CFG
• Loader: Qwen3_5ForConditionalGeneration.from_pretrained (multimodal-preserved class)
• Calibration: neuralmagic/calibration LLM split, 20 samples × 8192 tokens
• Excluded from quantization (kept BF16) — XS variant differences from the regular variant in bold:
  • lm_head, proj_out.*, *router*, *mlp.gate.* (NVFP4_DEFAULT_CFG)
  • *linear_attn.conv1d*, *mixer.conv1d* *(NVFP4_DEFAULT_CFG default — kept BF16 because FP4 quantization of the SSM 1D convolution causes drift on long-context recurrence; this is the recurrence-critical kernel of the GatedDeltaNet block. Both regular and XS variants preserve this.)*
  • *linear_attn* is NOT broadly excluded (XS difference — the projection matmuls in_proj_qkv, in_proj_z, in_proj_a/b, out_proj get NVFP4-quantized; saves ~8 GB; FP4 is a clean win on bandwidth-bound matmuls)
  • *visual* (vision tower preservation)
  • *mtp* (MTP head preservation)
  • *output_layer*, output.*
• MTP graft: 15 tensors copied bf16 from Qwen/Qwen3.6-27B after modelopt export
• Pipeline: lna-lab/GGUF-to-NVFP4-SM120 reference recipe, adapted for AEON-Ultimate-BF16 input + separate MTP source

Provenance & credits

• BF16 source: AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16. See that card for the full abliteration pipeline.
• MTP graft technique: lna-lab/GGUF-to-NVFP4-SM120 (docs/MTP_GRAFT_RECIPE.md)
• Reference benchmark recipes: sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP
• Quantization: NVIDIA TensorRT Model Optimizer (nvidia-modelopt 0.43.0)
• Base: Alibaba Qwen team — Qwen/Qwen3.6-27B

License + responsibility

Apache 2.0, inherited from Qwen/Qwen3.6-27B. This is an uncensored model. Read the full User Responsibility & Arbitration Clause on the BF16 source card before deploying. Summary: you implement downstream safety layers (input validation, output filtering, content moderation, audit logging, rate limiting, access controls, human-in-the-loop for high-risk workflows). The model has no opinions of its own — you supply the opinions, the judgment, and the ethics.

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