Qwen2 7B — Hardware Requirements & GPU Compatibility
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Qwen2 7B is a 7.6B-parameter open language model from Alibaba in the Qwen 2 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 4.99 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 · ~131 tok/s · 128K)Macs from 8 GB (IQ3_S · ~20 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2 7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/Qwen2-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.2 GB free · 16K context
Download: Qwen2-7B.Q4_K_M.gguf4.68 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (6 GB headroom) · Q4_K_M
- Generation speed
- ~47 tok/s
- generation speed
- Cost per 1M output tokens
- $0.45
- per 1M output tokens
How Much VRAM Does Qwen2 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.6 GB | 5.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~180 tok/s | |
| 3.8 GB | 5.5 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~175 tok/s | |
| 4.1 GB | 5.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~159 tok/s | |
| 4.2 GB | 6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~155 tok/s | |
| 5.0 GB | 6.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~131 tok/s | |
| 5.8 GB | 7.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~112 tok/s | |
| 6.7 GB | 8.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~98 tok/s | |
| 8.0 GB | 9.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~82 tok/s |
Selected Q4_K_M
4-bit medium quantization5.8GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.2 GB free · ~131 tok/s
≥50 tok/s needs ≥383 GB/s · RTX 5090 ~233, RTX 3090 Ti ~131
Which Hardware Can Run Qwen2 7B?
Q4_K_M · 5.0 GBQwen2 7B (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 7.4 GB, bringing total usage to 12.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Qwen2 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 2.2 GB free · up to 32K ctx
16 GPUs · 2 device families
RTX 3080~99Simulate output on RTX 3080RTX 2080 Ti~80Simulate output on RTX 2080 TiRTX 3070 Ti~79Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 14.2 GB free · up to 128K ctx
1 GPU
RX 7900 XT~96Simulate output on RX 7900 XT32–40 GB
fits · 26.2 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~233Simulate output on RTX 5090M5 Max36 GB~65Simulate output on Mac Studio M5 Max (36 GB)M532 GB~22Simulate output on MacBook Air 13" M5 (32 GB)Orin~27Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~18Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen2 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
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent40.7%
#101/145 open
Alternatives to Qwen2 7B
Qwen 2 family · 13 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 |
Fine-tunes & variants(1)
Specifications
- Publisher
- Alibaba
- Family
- Qwen 2
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-06-04
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen2 7B need?
Qwen2 7B requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 131K context adds up to 7.4 GB (12.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 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 ≈ 7.8 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Qwen2 7B?
For Qwen2 7B, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2 7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2 7B, the smallest at IQ3_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.0 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Qwen2 7B locally?
Yes — Qwen2 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen2 7B?
At Q4_K_M, Qwen2 7B can reach ~962 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~131 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 ÷ 4.99 × 0.65 = ~1042 tok/s
Estimated speed at Q4_K_M (5.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen2 7B?
At Q4_K_M, the download is about 4.57 GB. The full-precision BF16 version is 15.23 GB. The smallest option (IQ2_XXS) is 2.09 GB.
Which GPUs can run Qwen2 7B?
52 consumer GPUs can run Qwen2 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 Qwen2 7B?
151 devices with unified memory can run Qwen2 7B at Q4_K_M (5.0 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.