Qwen3.8 27B EXL3 3.5bpw — Hardware Requirements & GPU Compatibility
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Qwen3.8 27B EXL3 3.5bpw is a 7.7B-parameter open language model from Mia-AiLab in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 16.09 GB of VRAM — see which GPUs and Macs can run it below.
1.6K downloads
47 likesSpecifications
- Publisher
- Mia-AiLab
- Family
- Qwen 3.8
- Parameters
- 7.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-08-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.8 27B EXL3 3.5bpw Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 16.1 GB | 72.9 GB | 15.34 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen3.8 27B EXL3 3.5bpw?
BF16 · 16.1 GBQwen3.8 27B EXL3 3.5bpw (BF16) requires 16.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 72.9 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3.8 27B EXL3 3.5bpw?
BF16 · 16.1 GB41 devices with unified memory can run Qwen3.8 27B EXL3 3.5bpw, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.8 27B EXL3 3.5bpw need?
Qwen3.8 27B EXL3 3.5bpw requires 16.1 GB of VRAM at BF16. Full 262K context adds up to 56.8 GB (72.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.7B × 16 bits ÷ 8 = 15.3 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 262K context)
VRAM usage by quantization
BF1616.1 GBBF16 + full context72.9 GB- Can I run Qwen3.8 27B EXL3 3.5bpw on a Mac?
Qwen3.8 27B EXL3 3.5bpw requires at least 16.1 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3.8 27B EXL3 3.5bpw locally?
Yes — Qwen3.8 27B EXL3 3.5bpw can run locally on consumer hardware. At BF16 quantization it needs 16.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.8 27B EXL3 3.5bpw?
At BF16, Qwen3.8 27B EXL3 3.5bpw can reach ~298 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~41 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 16.1 × 0.65 = ~323 tok/s
Estimated speed at BF16 (16.1 GB)
~323 tok/s~41 tok/s~323 tok/s~298 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.8 27B EXL3 3.5bpw?
At BF16, the download is about 15.34 GB.
- Which GPUs can run Qwen3.8 27B EXL3 3.5bpw?
8 consumer GPUs can run Qwen3.8 27B EXL3 3.5bpw at BF16 (16.1 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.8 27B EXL3 3.5bpw?
41 devices with unified memory can run Qwen3.8 27B EXL3 3.5bpw at BF16 (16.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.