Qwen3.5 27B — Hardware Requirements & GPU Compatibility
Vision
Qwen3.5 27B is a 27.8-billion-parameter dense model from Alibaba's Qwen team, built for both text and image input using the same hybrid linear/full-attention architecture as the rest of the Qwen3.5 family. As a vision-capable model it can describe and reason about images alongside prompts, suiting multimodal chat and document tasks. At this size, local inference calls for quantization and a single high-end 24GB-plus consumer or workstation GPU rather than budget hardware. It supports a 262K token context window for long documents and multi-turn conversations, and is released under the Apache 2.0 license for unrestricted commercial and research use. Published in late February 2026, it sits alongside a separate small-model tier (0.8B–9B) in the same family, giving developers a mid-sized, single-GPU-friendly dense option.
1.9M downloads
1.1K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3.5
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-02-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.5 27B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 12.6 GB | 69.4 GB | 11.81 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 12.9 GB | 69.7 GB | 12.15 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.3 GB | 71.1 GB | 13.54 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 14.6 GB | 71.5 GB | 13.89 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 17.4 GB | 74.2 GB | 16.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 20.5 GB | 77.4 GB | 19.79 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 23.7 GB | 80.5 GB | 22.92 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 28.5 GB | 85.3 GB | 27.78 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 27B?
Q4_K_M · 17.4 GBQwen3.5 27B (Q4_K_M) requires 17.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 74.2 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 Qwen3.5 27B?
Q4_K_M · 17.4 GB41 devices with unified memory can run Qwen3.5 27B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3.5 27B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 27B need?
Qwen3.5 27B requires 17.4 GB of VRAM at Q4_K_M, or 56.3 GB at BF16. Full 262K context adds up to 56.8 GB (74.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M17.4 GBQ4_K_M + full context74.2 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B?
Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.5 27B?
For Qwen3.5 27B, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (17.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.
VRAM requirement by quantization
IQ2_XXS8.4 GBQ2_K12.6 GBIQ4_XS15.7 GBQ4_K_M ★17.4 GBQ4_K_L17.8 GBBF1656.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 27B on a Mac?
Qwen3.5 27B requires at least 8.4 GB at IQ2_XXS, 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 Qwen3.5 27B locally?
Yes — Qwen3.5 27B can run locally on consumer hardware. At Q4_K_M quantization it needs 17.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 27B?
At Q4_K_M, Qwen3.5 27B can reach ~276 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.4 × 0.65 = ~299 tok/s
Estimated speed at Q4_K_M (17.4 GB)
~299 tok/s~38 tok/s~299 tok/s~276 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.5 27B?
At Q4_K_M, the download is about 16.67 GB. The full-precision BF16 version is 55.56 GB. The smallest option (IQ2_XXS) is 7.64 GB.
- Which GPUs can run Qwen3.5 27B?
8 consumer GPUs can run Qwen3.5 27B at Q4_K_M (17.4 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 Qwen3.5 27B?
41 devices with unified memory can run Qwen3.5 27B at Q4_K_M (17.4 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.