Qwen3 1.7B — Hardware Requirements & GPU Compatibility
ChatFunctions
Qwen3 1.7B is a 1.7-billion parameter instruction-tuned model from Alibaba Cloud's Qwen 3 series. It is a lightweight model designed for deployment on minimal hardware, including low-VRAM GPUs and even CPU-only configurations with acceptable latency. Despite its compact size, it supports hybrid thinking mode and handles basic conversational tasks, simple question answering, and text generation. The model is useful for edge deployment, embedded applications, and scenarios where fast inference with minimal resource consumption is the priority. It represents a significant quality improvement over Qwen 2.5 at the sub-2B scale. Released under the Apache 2.0 license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 40K)Best on 24 GB (Q4_K_M · ~374 tok/s · 40K)Macs from 8 GB (Q4_K_M · ~41 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 1.7B
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
ollama run qwen3:1.7bQ4_K_M fits fully on the RTX 4090 · 20.6 GB free · 16K context
Download: Qwen3-1.7B-Q4_K_M.gguf1.11 GBunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~134 tok/s
- generation speed
- Cost per 1M output tokens
- $0.12
- per 1M output tokens
How Much VRAM Does Qwen3 1.7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.4 GB | 4.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~468 tok/s | |
| 1.4 GB | 4.9 GB | 3-bit small — 3-bit small quantization | Fits: 40K context · ~461 tok/s | |
| 1.5 GB | 5.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~428 tok/s | |
| 1.6 GB | 5.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 40K context · ~423 tok/s | |
| 1.8 GB | 5.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~374 tok/s | |
| 2.0 GB | 5.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~331 tok/s | |
| 2.2 GB | 5.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~297 tok/s | |
| 2.6 GB | 6.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~255 tok/s |
Selected Q4_K_M
4-bit medium quantization3.4GBat 16K context
- Weights
- 1.2 GB
- KV cache
- 1.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~374 tok/s
≥50 tok/s needs ≥134 GB/s · RTX 5090 ~666, RTX 3090 Ti ~374
Which Hardware Can Run Qwen3 1.7B?
Q4_K_M · 1.8 GBQwen3 1.7B (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 41K context window can add up to 4.5 GB, bringing total usage to 6.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3 1.7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.6 GB free · up to 40K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~282Simulate output on RTX 3080RTX 2080 Ti~229Simulate output on RTX 2080 TiRTX 3070 Ti~226Simulate output on RTX 3070 TiM3*8 GB~41Simulate output on iMac 24" M3 (8 GB)Orin Nano~38Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~27Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 40K ctx
1 GPU
RX 7900 XT~274Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~666Simulate output on RTX 5090M5 Max36 GB~184Simulate output on Mac Studio M5 Max (36 GB)M532 GB~61Simulate output on MacBook Air 13" M5 (32 GB)Orin~76Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~50Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen3 1.7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Apr 2025Independent
Independent38.0%
#74/100 open
- AIME 2024/2025
Epoch AI · Apr 2025Independent
Independent8.1%
#63/88 open
Alternatives to Qwen3 1.7B
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 2BLiquid AI | — | 2.3 GB | ~423 | same size |
| LFM3 Omni 2BLiquid AI | — | 2.4 GB | ~385 | 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 |
| Helium 1 2Bkyutai | — | 2 GB | ~374 | same size |
Fine-tunes & variants(5)
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 2.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-27
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 1.7B need?
Qwen3 1.7B requires 1.8 GB of VRAM at Q4_K_M, or 4.6 GB at BF16. Full 41K context adds up to 4.5 GB (6.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.0B × 4.8 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 5 GB (at full 41K context)
VRAM usage by quantization
What's the best quantization for Qwen3 1.7B?
For Qwen3 1.7B, 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 1.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 1.7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3 1.7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 1.7B locally?
Yes — Qwen3 1.7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 1.7B?
At Q4_K_M, Qwen3 1.7B can reach ~2743 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~374 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 ÷ 1.75 × 0.65 = ~2971 tok/s
Estimated speed at Q4_K_M (1.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 1.7B?
At Q4_K_M, the download is about 1.22 GB. The full-precision BF16 version is 4.06 GB. The smallest option (IQ2_XXS) is 0.56 GB.
Which GPUs can run Qwen3 1.7B?
52 consumer GPUs can run Qwen3 1.7B 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 1.7B?
163 devices with unified memory can run Qwen3 1.7B at Q4_K_M (1.8 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.