LocalClaw/LLM/Qwen3.8-27B
Open-weight local LLM

Qwen3.8-27B

Catalogue summary: Official Qwen dense 27B vision-language release with Apache 2.0 weights, 262K native context, thinking controls and strong agentic coding benchmarks. Practical local path through Unsloth and LM Studio-compatible GGUF artifacts.

Repository editorial metadata; verify comparative claims in the linked upstream material.

32 GB catalogue minimum32 GB RAMQ4_K_MCoding assistant
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Parameters
27B
Minimum RAM
32 GB
Model size
16.8 GB
Quantization
Q4_K_M

Can Qwen3.8-27B run locally?

Qwen3.8-27B has a catalogue minimum of 32 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use qwen3.8-27b as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

Model sourcePublic GGUF repository (verified 2026-09-07): unsloth/Qwen3.8-27B-GGUFUpstream releasePaper or model cardLicence text
chatcodereasoningvisionagenticpowerlong-context

Install path

01
Check RAM fitMinimum 32 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch qwen3.8-27b in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: qwen
  • Parameters: 27B
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 32 GB
  • Catalogue model size: 16.8 GB
  • Tags: chat, code, reasoning, vision, agentic, power, long-context

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.

Catalogue tags

  • chat
  • code
  • reasoning
  • vision
  • agentic
  • power

Capability profile

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

speed
5
quality
9
coding
10
reasoning
10

Technical notes

Developer
Alibaba Cloud (Qwen Team)
License
Apache 2.0
Context window
262,144 tokens
Architecture
Dense Qwen3.8 vision-language causal model with 27B language parameters, Gated DeltaNet and gated attention layers, MTP training, native 262K context and optional YaRN extension toward 1M-token workflows.

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Comfortable headroomMac mini M4 Pro 48GBMobile workstationMacBook Pro M4 Max 36GBPower-user picks32GB RAM guide

Related catalogue entries

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

Qwen 3.6 35B-A3B 35B (3B active, MoE)Qwen 3 (32B) 32BQwen 3.5 (27B) 27BQwen 3.6 (27B) 27BQwen AgentWorld 35B-A3B 35B (3B active, MoE)Qwen 3.5 MoE (35B/3B active) 35B (3B active)Qwen 3 (14B) 14BQwen 3 VL (32B) 32B

Where to go next