Qwen3.8-27B FP8-BLOCK

Data-free (RTN) compressed-tensors FP8_BLOCK quantization of Qwen/Qwen3.8-27B @ 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0, with all Gated DeltaNet linear-attention projections kept in BF16. Built 2026-08-17 with llmcompressor model_free_ptq, scheme FP8_BLOCK (static 128x128 E4M3 weight blocks, dynamic group-128 FP8 input activations).

The original Qwen/Qwen3.8-27B model card is preserved unchanged in README-upstream.md and is the authority on the base model's capabilities, usage, and license.

Recipe

Same policy as the official Qwen/Qwen3.8-27B-FP8 except the official export quantizes the GDN in_proj_qkv/z/out_proj projections to FP8; this checkpoint does not. Weights 33.9 GiB (official FP8: 28.75 GiB). Build ~1.6 min on one RTX PRO 6000 Blackwell. The per-tensor composition is in quant-manifest.json.

Measured quality — distribution fidelity (v5 shard-0 KLD replay)

KL(BF16 base ‖ candidate) over 512 contexts / 1,048,064 positions, full 248,320 vocab, one shared BF16 LM head. Every checkpoint below was captured and replayed on the same hardware and protocol (RTX PRO 6000 Blackwell, vllm 0.27.1, bf16 KV, eager, max_num_seqs=1, one 2,048-token prefill chunk per context). Absolute values are suite-specific; orderings and differences between rows are the reliable signal (resolvable to ~1e-3).

Determinism: the harness is deterministic. Two independent captures of RadixArk agreed to Δmean-KLD = 3.3e-5, Δmedian = 1.5e-5, Δtop-1 = 3e-5 (all well inside the 512-context bootstrap CI, ±2e-3) — single-run values are reportable as-is. p99 is shown rather than max because max is one degenerate token-position (RadixArk max 10.34 ≈ 22x its p99) and not reproducible; p99 is the robust worst-case tail.

Reading the table: this checkpoint is the closest locally-measured candidate to the BF16 base, ~0.001 lower mean KLD than the official FP8 export (the entire gain is the GDN-BF16 projection policy), and ~10x tighter than the NVFP4 rows we measured. Both NVFP4 rows are measurably worse (aggregate MLP NVFP4 error dominates on the 64-layer MoE); among the two, unsloth/Qwen3.8-27B-NVFP4 is notably better than RadixArk (mean 0.030 vs 0.042). Toolchain validated by reproducing the published official-FP8 row (0.005189 vs published 0.005197) and the published unsloth row (0.030155 vs 0.030115) to <5e-5 in the same session.

Serving performance — SGLang MTP (2026-08-17, RTX PRO 6000)

Matched methodology across all rows: 128 ShareGPT prompts, seed 42, 32768-token context, FP8 KV cache, flashinfer, MTP3 (SGLang EAGLE 3-step / topk-1 / 4 draft tokens), language-only, thinking off. All 128 requests completed in every row with 0 failures.

As of 2026-08-17 this model with MTP runs measurably faster under SGLang than vLLM (+7% c=1 decode, +19% c=32 decode). vLLM MTP acceptance 59.8–61.3%; SGLang MTP acceptance isn't reported through the vLLM bench client (same reporting gap as vLLM historical rows) — the decode speedup above is the direct measured benefit.

Memory / draft-copy status (checked 2026-08-17): SGLang 0.5.17 EAGLE with a same-checkpoint draft (--speculative-draft-model-path <this model>) still loads a full separate draft weight copy (is_draft_worker=True, draft weights counted by the KV configurator) — not patched in this build. Target + draft ≈ 67.8 GB, leaving ~17 GB KV at mem-fraction 0.95 (≈ one full-length 262,144 request). The DFlash/DSPark small-draft workers exist in 0.5.17 (the path that would fix this) but a matching small draft for Qwen3.8 is not yet staged here; multi-stream agentic stays on NVFP4 until then. vLLM MTP shares target weights (no second copy), so it fits but is slower.

Replicating the quantization

The checkpoint was built with data-free (RTN) model-free PTQ, no calibration examples or architecture-specific Transformers code. The reproducible core call with llmcompressor is:

from llmcompressor import model_free_ptq

model_free_ptq(
    model_stub="snapshot of Qwen/Qwen3.8-27B pinned to 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
    save_directory="./Qwen3.8-27B-FP8-BLOCK",
    scheme="FP8_BLOCK",
    ignore=[
        # official export's GDN state path + norms/head/tower etc.
        "re:.*lm_head$",
        "re:.*embed_tokens$",
        "re:.*linear_attn.*",      # keep all GDN linear-attn (incl. proj) BF16
        "re:.*mlp\.gate$",
        "re:.*mlp\.shared_expert_gate$",
        "re:.*mtp\.fc$",
        "re:.*norm$",
        "re:.*visual.*",
        # plus the GDN state path: A_log, conv1d, dt_bias, in_proj_a/b/ba
    ],
    max_workers=1,
    device="cuda:0",
)

The ignore policy is derived from the official Qwen/Qwen3.8-27B-FP8 882-entry ignore list; the only structural difference from the official export is that the GDN in_proj_qkv/z/out_proj projections are excluded (kept BF16) rather than quantized. Saved scheme is compressed-tensors FP8_BLOCK; per-tensor composition is in quant-manifest.json.

Serving with SGLang MTP

On NVIDIA Blackwell / SM120 GPUs both engines need the hybrid-architecture recipe (--language-model-only, --mamba-ssm-dtype bfloat16 for the GDN SSM state, --kv-cache-dtype fp8_e4m3, and the FlashInfer version-check disclaimer; SGLang also --max-mamba-cache-size = max-running-requests × (5+4) for MTP so the GDN state pool does not clamp concurrency).

# SGLang MTP (fastest measured lane, EAGLE 3-step / topk-1 / 4 draft tokens)
python -m sglang.launch_server \
  --model-path ./Qwen3.8-27B-FP8-BLOCK \
  --trust-remote-code --tp-size 1 \
  --context-length 32768 --mem-fraction-static 0.90 \
  --max-running-requests 32 \
  --attention-backend flashinfer \
  --mamba-ssm-dtype bfloat16 \
  --kv-cache-dtype fp8_e4m3 \
  --reasoning-parser qwen3 \
  --language-only \
  --speculative-algorithm EAGLE \
  --speculative-draft-model-path ./Qwen3.8-27B-FP8-BLOCK \
  --speculative-num-steps 3 --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4

vLLM MTP uses the same target + --speculative-config '{"method":"mtp","num_speculative_tokens":3}'.

Runtime notes

• Loads as compressed-tensors in vLLM 0.27.1 and SGLang 0.5.17.
• SGLang EAGLE same-checkpoint draft loads a full draft copy (see above); vLLM MTP shares target weights.
• Not measured: multimodal generation quality, long-context quality, safety, task-level benchmark deltas vs BF16 (distribution fidelity only).