Qwen3.5 2B — Hardware Requirements & GPU Compatibility
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Qwen3.5 2B is a 2B-parameter open language model from titan0115 in the Qwen 3.5 family. At Q4_K_M it needs about 1.32 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~496 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~54 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 2B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve titan0115/Qwen3.5-2B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~449 tok/s
- generation speed
- Cost per 1M output tokens
- $0.076
- per 1M output tokens
How Much VRAM Does Qwen3.5 2B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.9 GB | 4.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~697 tok/s | |
| 1.0 GB | 4.7 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~683 tok/s | |
| 1.1 GB | 4.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~612 tok/s | |
| 1.1 GB | 4.8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~596 tok/s | |
| 1.3 GB | 5.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~496 tok/s | |
| 1.6 GB | 5.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~417 tok/s | |
| 1.8 GB | 5.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~360 tok/s | |
| 2.2 GB | 5.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~298 tok/s |
Selected Q4_K_M
4-bit medium quantization3.1GBat 16K context
- Weights
- 0.8 GB
- KV cache
- 2.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.9 GB free · ~496 tok/s
≥50 tok/s needs ≥101 GB/s · RTX 5090 ~882, RTX 3090 Ti ~496
Which Hardware Can Run Qwen3.5 2B?
Q4_K_M · 1.3 GBQwen3.5 2B (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3.5 2B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.9 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~374Simulate output on RTX 3080RTX 2080 Ti~303Simulate output on RTX 2080 TiRTX 3070 Ti~300Simulate output on RTX 3070 TiM3*8 GB~54Simulate output on iMac 24" M3 (8 GB)Orin Nano~50Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~36Simulate output on iPhone 1720 GB
fits · 16.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~364Simulate output on RX 7900 XT32–40 GB
fits · 28.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~882Simulate output on RTX 5090M5 Max36 GB~244Simulate output on Mac Studio M5 Max (36 GB)M532 GB~82Simulate output on MacBook Air 13" M5 (32 GB)Orin~101Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~67Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.5 2B
Qwen 3.5 family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 2BLiquid AI | — | 2.3 GB | ~423 | same size |
| LFM3 VL 2BLiquid AI | — | 2.3 GB | ~423 | same size |
| Qwen3 VL 2B InstructAlibaba | — | 3.5 GB | ~362 | same size |
| Moondream2vikhyatk | — | 3 GB | ~516 | same size |
| OneRec 1.7BOpenOneRec | — | 3.5 GB | ~362 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
Specifications
- Publisher
- titan0115
- Family
- Qwen 3.5
- Parameters
- 2B
- Release Date
- 2026-03-02
Frequently Asked Questions
How much VRAM does Qwen3.5 2B need?
Qwen3.5 2B requires 1.3 GB of VRAM at Q4_K_M, or 4.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2B × 4.8 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Qwen3.5 2B?
For Qwen3.5 2B, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_1 (1.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 0.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.5 2B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3.5 2B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.5 2B locally?
Yes — Qwen3.5 2B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 2B?
At Q4_K_M, Qwen3.5 2B can reach ~3636 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~496 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 ÷ 1.32 × 0.65 = ~3939 tok/s
Estimated speed at Q4_K_M (1.3 GB)
Real-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 1.20 GB. The full-precision BF16 version is 4.00 GB. The smallest option (IQ2_M) is 0.68 GB.
Which GPUs can run Qwen3.5 2B?
52 consumer GPUs can run Qwen3.5 2B at Q4_K_M. 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?
163 devices with unified memory can run Qwen3.5 2B at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.