LocalClaw/LLM/Ling-2.6-flash (104B MoE)
Public model card reference

Ling-2.6-flash (104B MoE)

Public model card. Check the source status below before planning a local setup.

Public model cardNo public GGUF verified104B (7.4B active)2026-04
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Source status
Public model card
Public GGUF
Not verified
Parameters
104B (7.4B active)
Access
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Verified source status

Catalogue summary: InclusionAI's MIT-licensed instruct MoE optimized for fast agent workloads. 104B total parameters, only 7.4B active, hybrid linear attention, 262K context and strong tool-use / multi-step execution with high token efficiency.

LocalClaw verified a public model card for Ling-2.6-flash (104B MoE) on 2026-11-06, but no public GGUF file in that repository. The catalogue RAM and quantization fields are estimates, not a verified install path.

Only the public model card was verified; no public GGUF file was verified in that repository. Open the model card to confirm current artefacts and supported runtimes. LocalClaw does not claim a one-click LM Studio install.

Public model card (no GGUF file verified 2026-11-06): inclusionAI/Ling-2.6-flashUpstream releasePaper or model cardLicence text
chatcodereasoningspeedquality

Source availability

01
Open the model cardUse the verified upstream card as the source of truth for this record.
02
Inspect current filesNo public GGUF file was verified in the repository during the catalogue audit.
03
Confirm a supported runtimeChoose a local runtime only after verifying the exact artefact, licence and hardware requirements upstream.

Catalogue record

  • Family: ling
  • Parameters: 104B (7.4B active)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 80 GB
  • Catalogue model size: 65 GB
  • Tags: chat, code, reasoning, speed, quality

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Reading the scores

LocalClaw /10 is an editorial catalogue rating. Community /5 comes from member reviews. Your machine fit is a separate memory and runtime assessment.

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
8
quality
9
coding
9
reasoning
8

Technical notes

Developer
InclusionAI / Ant Group
License
MIT
Context window
262,144 tokens
Architecture
Sparse MoE instruct model — 104B total parameters, 7.4B active. Hybrid linear attention using a 1:7 MLA + Lightning Linear architecture, optimized for high-throughput agent inference.

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

Ling-3.0-tiny 7.9B (1.3B active, MoE)Ling Flash Base 2.0 106B (6.1B active, MoE)Qwen 3.5 MoE (122B/10B active) 122B (10B active)Qwen 3 Next (80B/3B MoE) 80B (3B active)Llama-3.3-Nemotron-Super (49B) 49BCommand R+ (104B) 104BDeepSeek V4 Flash Vision Exp 284B (13B active, multimodal MoE)Gemma 4 31B 31B

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