Qwen 7B — Hardware Requirements & GPU Compatibility
Chat
Qwen-7B is Alibaba's original 7.7-billion-parameter base language model from the first Qwen series, pretrained (not instruction-tuned) on over 2.4 trillion tokens of Chinese, English, multilingual, code, and math text, with a roughly 150,000-token vocabulary aimed at multilingual friendliness. Alibaba positions it as a foundation for further fine-tuning; the aligned chat assistant Qwen-7B-Chat is a separate release built on top of it. Its native training context is 8,192 tokens, and the card documents NTK-aware interpolation, LogN attention scaling, and windowed attention to extrapolate usable context out to 32,768 tokens. At under 8 billion parameters, it fits on a single consumer GPU. Context length is 8,192 tokens natively, extendable to 32,768 tokens with the NTK, LogN, and window-attention techniques above. It is released under the Tongyi Qianwen License Agreement, which allows academic use freely but requires applying for a commercial license for commercial deployment. It was published in August 2023, an early entry in a Qwen lineage now several generations ahead.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~129 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~28 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen 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 hf.co/mradermacher/Qwen-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.4 GB free · 16K context
Download: Qwen-7B.Q4_K_M.gguf4.90 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~116 tok/s
- generation speed
- Cost per 1M output tokens
- $0.29
- per 1M output tokens
How Much VRAM Does Qwen 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.6 GB | 9.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~182 tok/s | |
| 3.7 GB | 9.2 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~176 tok/s | |
| 4.1 GB | 9.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~158 tok/s | |
| 4.3 GB | 9.7 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~154 tok/s | |
| 5.1 GB | 10.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~129 tok/s | |
| 6.0 GB | 11.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~108 tok/s | |
| 7.0 GB | 12.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~94 tok/s | |
| 8.5 GB | 13.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~77 tok/s |
Selected Q4_K_M
4-bit medium quantization7.6GBat 16K context
- Weights
- 4.4 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.4 GB free · ~129 tok/s
≥50 tok/s needs ≥392 GB/s · RTX 5090 ~228, RTX 3090 Ti ~129
Which Hardware Can Run Qwen 7B?
Q4_K_M · 5.1 GBQwen 7B (Q4_K_M) requires 5.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Qwen 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.4 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~97Simulate output on RTX 3080RTX 2080 Ti~79Simulate output on RTX 2080 TiRTX 3070 Ti~78Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~94Simulate output on RX 7900 XT32–40 GB
fits · 24.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~228Simulate output on RTX 5090M5 Max36 GB~63Simulate output on Mac Studio M5 Max (36 GB)M532 GB~21Simulate output on MacBook Air 13" M5 (32 GB)Orin~26Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen 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 6 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU
Epoch AI · Sep 2023Lab-reported
Lab-reported45.0%
#66/85 open
- BIG-Bench Hard
Epoch AI · Sep 2023Lab-reported
Lab-reported45.0%
#24/38 open
- GSM8K
Epoch AI · Sep 2023Lab-reported
Lab-reported51.7%
#32/62 open
- ARC Challenge
Epoch AI · Sep 2023Lab-reported
Lab-reported75.3%
#17/56 open
- BoolQ
Epoch AI · Sep 2023Lab-reported
Lab-reported76.4%
#20/38 open
- PIQA
Epoch AI · Sep 2023Lab-reported
Lab-reported77.9%
#26/40 open
Alternatives to Qwen 7B
Qwen family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
Specifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 7.7B
- Architecture
- QWenLMHeadModel
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2023-08-03
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen 7B need?
Qwen 7B requires 5.1 GB of VRAM at Q4_K_M, or 17.0 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7.7B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.5 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 Qwen 7B?
For Qwen 7B, Q4_K_M (5.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen 7B locally?
Yes — Qwen 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen 7B?
At Q4_K_M, Qwen 7B can reach ~941 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~129 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 ÷ 5.1 × 0.65 = ~1020 tok/s
Estimated speed at Q4_K_M (5.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen 7B?
At Q4_K_M, the download is about 4.63 GB. The full-precision BF16 version is 15.44 GB. The smallest option (IQ2_XXS) is 2.12 GB.
Which GPUs can run Qwen 7B?
52 consumer GPUs can run Qwen 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 Qwen 7B?
151 devices with unified memory can run Qwen 7B at Q4_K_M (5.1 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.