Qwen3.5 2B Text Only — Hardware Requirements & GPU Compatibility
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Qwen3.5 2B Text Only is a 1.9B-parameter open language model from principled-intelligence in the Qwen 3.5 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 4.16 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 256K)Best on 24 GB (BF16 · ~158 tok/s · 256K)Macs from 8 GB (BF16 · ~17 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.5 2B Text Only
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve principled-intelligence/Qwen3.5-2B-text-only --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · BF16
- Generation speed
- ~56 tok/s
- generation speed
- Cost per 1M output tokens
- $0.41
- per 1M output tokens
How Much VRAM Does Qwen3.5 2B Text Only Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | 4.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~158 tok/s |
Selected BF16
Brain floating point 164.3GBat 16K context
- Weights
- 3.8 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.7 GB free · ~158 tok/s
≥50 tok/s needs ≥320 GB/s · RTX 5090 ~280, RTX 3090 Ti ~158
Which Hardware Can Run Qwen3.5 2B Text Only?
BF16 · 4.2 GBQwen3.5 2B Text Only (BF16) requires 4.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 262K context window can add up to 3.1 GB, bringing total usage to 7.3 GB. 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 Text Only, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.7 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~119Simulate output on RTX 3080RTX 2080 Ti~96Simulate output on RTX 2080 TiRTX 3070 Ti~95Simulate output on RTX 3070 TiM3*8 GB~17Simulate output on iMac 24" M3 (8 GB)Orin Nano~16Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~12Simulate output on iPhone 1720 GB
fits · 15.7 GB free · up to 256K ctx
1 GPU
RX 7900 XT~115Simulate output on RX 7900 XT32–40 GB
fits · 27.7 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~280Simulate output on RTX 5090M5 Max36 GB~77Simulate output on Mac Studio M5 Max (36 GB)M532 GB~26Simulate output on MacBook Air 13" M5 (32 GB)Orin~32Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~21Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Qwen3.5 2B Text Only
Qwen 3.5 family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Moondream2vikhyatk | — | 3 GB | ~516 | same size |
| Qwen 1 8BAlibaba | — | 1.9 GB | ~542 | same size |
| JiRackUltra 1BCMSManhattan | — | 1.8 GB | ~461 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
| Llm Jp 3.1 1.8B Instruct4llm-jp | — | 2.2 GB | ~360 | same size |
| Helium 1 2Bkyutai | — | 2 GB | ~374 | same size |
Specifications
- Publisher
- principled-intelligence
- Family
- Qwen 3.5
- Parameters
- 1.9B
- Architecture
- Qwen3_5ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-11
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.5 2B Text Only need?
Qwen3.5 2B Text Only requires 4.2 GB of VRAM at BF16. Full 262K context adds up to 3.1 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.9B × 16 bits ÷ 8 = 3.8 GB
KV Cache + Overhead ≈ 0.4 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.
KV Cache + Overhead ≈ 3.5 GB (at full 262K context)
VRAM usage by quantization
Can I run Qwen3.5 2B Text Only on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Qwen3.5 2B Text Only. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 4.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.5 2B Text Only locally?
Yes — Qwen3.5 2B Text Only can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.5 2B Text Only?
At BF16, Qwen3.5 2B Text Only can reach ~1154 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~158 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 ÷ 4.16 × 0.65 = ~1250 tok/s
Estimated speed at BF16 (4.2 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 Text Only?
At BF16, the download is about 3.76 GB.
Which GPUs can run Qwen3.5 2B Text Only?
52 consumer GPUs can run Qwen3.5 2B Text Only at BF16. 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 Text Only?
163 devices with unified memory can run Qwen3.5 2B Text Only at BF16 (4.2 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.