Qwen3.5 2B Base — Hardware Requirements & GPU Compatibility
Vision
Qwen3.5-2B-Base is a 2.3-billion-parameter dense checkpoint in Alibaba's Qwen3.5 base model family, a native vision-language foundation model rather than a text-only model with vision bolted on. It is pretrain-only: fine-tuning, in-context-learning, or further research, not direct conversation, though its control tokens are compatible with the official chat template for efficient LoRA-style adaptation. Its hybrid architecture pairs Gated DeltaNet linear attention with periodic gated full-attention layers. At 2.3B parameters, it runs comfortably on a single consumer GPU, even unquantized. The model supports a native 262,144 token context window, extensible up to 1,010,000 tokens. It is released under the Apache 2.0 license, and was published in February 2026. Qwen3.5 introduced early-fusion multimodal pretraining that Alibaba says outperforms the separate Qwen3-VL models on reasoning, coding, and visual understanding.
208.7K downloads
93 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 Base 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 |
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 Qwen3.5 2B Base?
Q4_K_M · 1.8 GBQwen3.5 2B Base (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 Base?
Q4_K_M · 1.8 GB59 devices with unified memory can run Qwen3.5 2B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen3.5 2B Base
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 2B Base need?
Qwen3.5 2B Base 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 Base?
For Qwen3.5 2B Base, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.4 GB.
VRAM requirement by quantization
Q2_K1.4 GBQ3_K_L1.6 GBQ4_K_M ★1.8 GBQ5_01.8 GBQ5_K_S2.0 GBBF165.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 2B Base on a Mac?
Qwen3.5 2B Base requires at least 1.4 GB at Q2_K, 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 Base locally?
Yes — Qwen3.5 2B Base 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 Base?
At Q4_K_M, Qwen3.5 2B Base 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 Base?
At Q4_K_M, the download is about 1.36 GB. The full-precision BF16 version is 4.55 GB. The smallest option (Q2_K) is 0.97 GB.
- Which GPUs can run Qwen3.5 2B Base?
52 consumer GPUs can run Qwen3.5 2B Base 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 Base?
59 devices with unified memory can run Qwen3.5 2B Base 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.