Model record
Qwen3.8 27B
Alibaba Cloud · Apache 2.0 · verified
What this model is
The architecture is dense. Every parameter is part of the active path, so memory and speed scale with the full model rather than a smaller expert subset.
Memory and quantization
Decimal GB. Sizes combine the published GGUF weight file with the F16 multimodal projector required for image and video input.
| Quantization | Published files | 8K estimate | Smallest comfortable GPU | Smallest comfortable Mac | Context at smallest GPU / Mac | Speed estimate | Run commands |
|---|---|---|---|---|---|---|---|
| Q4_K_M | 17.39 GB | 19.43 GB | NVIDIA GeForce RTX 4090 24GB · 24 GB VRAM | Apple Mac mini M4 24GB · 24 GB unified memory | 77,937 / 77,937 tokens | Estimate · Heavy relative class | ollama run qwen3.8:27b-q4_K_M lms get "Qwen3.8 27B"@q4_k_m |
| Q8_0 | 29.98 GB | 32.01 GB | NVIDIA RTX 6000 Ada 48GB · 48 GB VRAM | Apple Mac mini M4 Pro 48GB · 48 GB unified memory | 252,137 / 252,137 tokens | Estimate · Heavy relative class | ollama run qwen3.8:27b-q8_0 lms get "Qwen3.8 27B"@q8_0 |
Estimate formula: published weight files + 0.5369 GB per 8K tokens × context / 8K + 1.5 GB runtime overhead. Sizes are decimal GB, not GiB. At 8K context, the smallest listed build is about 19.4 GB before OS and application reserve.
Full-attention KV estimate from the public config. Sixteen of 64 layers use full attention; linear-attention state is small and excluded from this conservative per-token figure.
Ollama tags were checked against the linked Ollama library pages on 2026-11-02. LM Studio's lms get suffix selects a file when the search results expose that exact quantization; confirm the local key with lms ls.
KV method source: https://huggingface.co/Qwen/Qwen3.8-27B/raw/main/config.json
Speed heuristic
Not benchmarked here. Dense 27B inference is limited by all 27B parameters on every token.
Active path: 27B parameters. This is a relative speed signal only; it is not a tokens/sec benchmark.
Runtimes
- Ollama
- llama.cpp
- MLX
- vLLM
ollama run qwen3.8:27blms get "Qwen3.8 27B"@q4_k_m
lms load <model-key-from-lms-ls> --gpu max --context-length 8192License restrictions
Apache 2.0 permits commercial use and modification; retain notices and comply with the patent and termination terms.
This is a plain-language summary, not legal advice. Read the linked license before commercial deployment or redistribution.
Known issues
- A Q4 build plus KV cache is close to a 20 GB memory floor, so 16 GB devices need a smaller model or aggressive offload.
- The GGUF package needs the multimodal projector for vision and video input.
If it does not fit, try these smaller records
These alternatives have a lower recommended memory target in the current catalog. Open each page to compare the quantization-specific fit before downloading.
gpt-oss-20b
MoE · starting at 14 GB
Qwen3 8B
Dense · starting at 8 GB
DeepSeek R1 8B
Dense · starting at 8 GB
Mistral 7B
Dense · starting at 8 GB
FAQ
Model-specific questions
What is the smallest local file for Qwen3.8 27B?
The smallest published weight package in this record is Q4_K_M at 17.39 GB. The 8K runtime estimate adds 0.54 GB of KV cache and 1.5 GB overhead, for about 19.4 GB before operating-system reserve.
How much memory do the published quantizations need at 8K?
Q4_K_M about 19.4 GB at 8K; Q8_0 about 32.0 GB at 8K. These are calculated estimates using the published weight sizes and the model's KV-cache assumption, not live memory measurements.
Which GPU and Mac configurations are comfortable for Qwen3.8 27B?
Against the current catalog, the smallest configurations that clear the 20% headroom target are: Q4_K_M: NVIDIA GeForce RTX 4090 24GB; Q8_0: NVIDIA RTX 6000 Ada 48GB. A larger configuration may still be preferable for longer context or faster memory bandwidth.
What is the right Ollama or LM Studio command for Qwen3.8 27B?
Use the command attached to the specific quantization in the table. Ollama tags are taken from the checked Ollama library pages. For LM Studio, use lms get with the displayed quantization suffix, then load the resulting local key from lms ls with --gpu max and the desired context length.
What should I run instead if Qwen3.8 27B does not fit?
Smaller catalog alternatives include gpt-oss-20b, Qwen3 8B, DeepSeek R1 8B, Mistral 7B. Compare their quantization tables and minimum memory before choosing; a smaller parameter count does not always mean a proportionally smaller download.
Sources
- https://huggingface.co/Qwen/Qwen3.8-27B
- https://huggingface.co/unsloth/Qwen3.8-27B-GGUF
- https://ollama.com/library/qwen3.8
- https://huggingface.co/Qwen/Qwen3.8-27B/raw/main/config.json
This page is an independent compatibility guide, not official documentation. Verify the primary source before downloading or deploying a model.