Qwen3.5 2B — Hardware Requirements & GPU Compatibility
Vision
Qwen3.5 2B is a 2.3-billion-parameter model from Alibaba's Qwen team, one of the smaller entries in the Qwen3.5 lineup (0.8B–9B) built to handle text and image input together. It uses a hybrid architecture mixing linear-attention layers with periodic full-attention layers to keep long-context inference efficient. As a vision-capable model it can read and reason about images alongside written prompts, and its small size lets it run on a laptop or even a phone once quantized, without a dedicated GPU. It supports an unusually long 262K token context window for its size. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in late February 2026 as part of Alibaba's push toward compact, natively multimodal edge models.
4.8M downloads
415 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3.5
- Parameters
- 2.3B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-02-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.5 2B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.4 GB | 14.2 GB | 0.97 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.4 GB | 14.2 GB | 0.99 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.5 GB | 14.3 GB | 1.11 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.5 GB | 14.3 GB | 1.14 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.8 GB | 14.6 GB | 1.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.0 GB | 14.8 GB | 1.62 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.3 GB | 15.1 GB | 1.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.7 GB | 15.5 GB | 2.27 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 2B?
Q4_K_M · 1.8 GBQwen3.5 2B (Q4_K_M) requires 1.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 14.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3.5 2B?
Q4_K_M · 1.8 GB59 devices with unified memory can run Qwen3.5 2B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen3.5 2B
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 2B need?
Qwen3.5 2B requires 1.8 GB of VRAM at Q4_K_M, or 5.0 GB at BF16. Full 262K context adds up to 12.8 GB (14.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.3B × 4.8 bits ÷ 8 = 1.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M1.8 GBQ4_K_M + full context14.6 GB- What's the best quantization for Qwen3.5 2B?
For Qwen3.5 2B, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (1.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.0 GB.
VRAM requirement by quantization
IQ2_XXS1.0 GBQ3_K_S1.4 GBIQ4_NL1.7 GBQ4_K_M ★1.8 GBQ5_K_S2.0 GBBF165.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 2B on a Mac?
Qwen3.5 2B requires at least 1.0 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 2B locally?
Yes — Qwen3.5 2B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 2B?
At Q4_K_M, Qwen3.5 2B can reach ~2712 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~370 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 ÷ 1.8 × 0.65 = ~2938 tok/s
Estimated speed at Q4_K_M (1.8 GB)
~2938 tok/s~370 tok/s~2938 tok/s~2712 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 2B?
At Q4_K_M, the download is about 1.36 GB. The full-precision BF16 version is 4.55 GB. The smallest option (IQ2_XXS) is 0.63 GB.
- Which GPUs can run Qwen3.5 2B?
52 consumer GPUs can run Qwen3.5 2B at Q4_K_M (1.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 2B?
59 devices with unified memory can run Qwen3.5 2B at Q4_K_M (1.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.