quant: nvfp4_nf3_hybrid — native serving for mixed NVFP4+NF3 MoE checkpoints

[MadeBy561]

Native serving for per-expert mixed-precision MoE checkpoints — the configuration behind
madeby561/GLM-5.2-MXFP8-NVFP4-NF3-Hybrid
(GLM-5.2 753B, all 256 experts/layer on 4× 96 GB SM120): top-64 experts NVFP4, 192-expert
tail NF3 3-bit, both tiers through the b12x W4A16 fused MoE kernel.

How it works: NvFp4Nf3HybridConfig extends the ModelOpt NVFP4 config and activates
automatically when a checkpoint's quantization config carries hybrid_bit_map (per-layer,
per-expert bit widths). Per MoE layer the method builds two prepared weight objects and
issues two preplanned launches sharing one route geometry and one topk_sum — CUDA-graph-safe,
with an M≤8 TC-decode direct-topk path (tc_zero_output=False accumulation on the second
launch). Layers absent from the map (e.g. the MTP head) run uniform NVFP4. An explicit
--quantization choice other than nvfp4_nf3_hybrid is respected — auto-detection only
applies when no quantization is forced.

No environment variables. Tier assignment comes from the checkpoint; scratch sizing from
max_num_batched_tokens. This replaces an out-of-tree runtime graft (site hook +
monkey-patch) that has served this exact configuration in production since 2026-07-05.

Depends on: b12x nf3_2p1 W4A16 layout — local-inference-lab/b12x#31. This PR should not merge
before that one.

Testing:

• Kernel-side numerical reference battery: b12x#31 tests/test_w4a16_nf3.py, 3/3 green.
• In-image (v19) import + detection tests passed: the real K64R16 checkpoint's config.json
  auto-selects nvfp4_nf3_hybrid (75-layer hybrid_bit_map read, no flags, no envs).
• Full zero-graft serving boot (pool, decode coherence incl. M≤8 TC path, MTP acceptance)
  runs later today; results will be posted as a comment.

Co-authored with Claude (Fable 5).

Summary by CodeRabbit

• New Features
  • Added support for the nvfp4_nf3_hybrid quantization method.
  • Enables hybrid mixture-of-experts models to use NVFP4/MXFP4 formats for selected experts and NF3 for others.
  • Added validation and configuration support for hybrid quantization metadata.
  • Improved runtime handling of hybrid expert execution across supported decode and prefill workloads.

[github-actions]

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ℹ️ Recent review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: 750d82ff-2e7b-4cdd-b065-fe2c04c4e81c

📥 Commits

Reviewing files that changed from the base of the PR and between c649d41 and 7721056.

📒 Files selected for processing (3)

• vllm/config/model.py
• vllm/model_executor/layers/quantization/__init__.py
• vllm/model_executor/layers/quantization/nvfp4_nf3_hybrid.py

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📝 Walkthrough

Walkthrough

Adds nvfp4_nf3_hybrid quantization detection and dispatch, with configuration parsing, tiered expert weight loading and repacking, shared runtime planning, and execution through NVFP4, NF3, or MXFP4 MoE paths.

Changes

Hybrid quantization support

Layer / File(s)	Summary
Method registration and configuration vllm/config/model.py, vllm/model_executor/layers/quantization/__init__.py, vllm/model_executor/layers/quantization/nvfp4_nf3_hybrid.py	Registers nvfp4_nf3_hybrid, resolves NvFp4Nf3HybridConfig, reads hybrid checkpoint settings, and defines hybrid runtime state.
Tiered weight loading and repacking vllm/model_executor/layers/quantization/nvfp4_nf3_hybrid.py	Classifies experts into kept and NF3 tiers, loads tier-specific tensors, and repacks them into b12x-compatible formats.
Launch planning and hybrid execution vllm/model_executor/layers/quantization/nvfp4_nf3_hybrid.py	Caches launch variants, initializes shared buffers, executes kept and NF3 tiers, optionally uses the MXFP4 modular kernel, and sums outputs.

Estimated code review effort: 4 (Complex) | ~60 minutes

Sequence Diagram(s)

sequenceDiagram
  participant NvFp4Nf3HybridMoEMethod
  participant HybridSharedRuntime
  participant B12xFusedMoE
  participant MXFP4ModularKernel
  NvFp4Nf3HybridMoEMethod->>HybridSharedRuntime: Ensure buffers and launch plans
  NvFp4Nf3HybridMoEMethod->>B12xFusedMoE: Execute NVFP4 and NF3 tiers
  NvFp4Nf3HybridMoEMethod->>MXFP4ModularKernel: Execute kept MXFP4 tier when configured
  B12xFusedMoE-->>NvFp4Nf3HybridMoEMethod: Return tier outputs
  MXFP4ModularKernel-->>NvFp4Nf3HybridMoEMethod: Return kept-tier output

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Possibly related PRs

• local-inference-lab/vllm#83: Updates the same ordered quantization override mechanism with another quantization method alias.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly matches the main change: adding native serving support for nvfp4_nf3_hybrid mixed NVFP4+NF3 MoE checkpoints.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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[voipmonitor]

Superseded by #104, which rebases the same implementation onto the current dev/fathomless-firmament head and adds focused CPU unit coverage for detection, parsing, validation, and NF3 unpacking. Closing this old-base PR so Luke has one canonical merge target.