Qwen1.5 7B — Hardware Requirements & GPU Compatibility
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Qwen1.5-7B is Alibaba's dense 7.7-billion-parameter base language model, part of the Qwen1.5 series that served as a beta step between the original Qwen and Qwen2. It is a decoder-only Transformer using SwiGLU activation, released as a pretrained checkpoint rather than a chat model; its card explicitly recommends applying further post-training such as SFT or RLHF rather than prompting it directly for text generation. It offers a stable 32K token context window and is distributed under Alibaba's Tongyi Qianwen license, a custom license rather than a fully open one. At 7.7 billion parameters, it runs comfortably on an 8-12 GB consumer GPU once quantized to 4-bit, making it an accessible option for local fine-tuning experiments.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~109 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~23 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen1.5 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/Qwen1.5-7B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 10.5 GB free · 16K context
Download: Qwen1.5-7B.i1-Q4_K_M.gguf4.77 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 3090 (24 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~101 tok/s
- generation speed
- Cost per 1M output tokens
- $0.50
- per 1M output tokens
How Much VRAM Does Qwen1.5 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.7 GB | 20.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~141 tok/s | |
| 4.8 GB | 20.9 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~138 tok/s | |
| 5.1 GB | 21.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~128 tok/s | |
| 5.2 GB | 21.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~125 tok/s | |
| 6.0 GB | 22.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~109 tok/s | |
| 6.9 GB | 23 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~95 tok/s | |
| 7.7 GB | 23.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~85 tok/s | |
| 9.1 GB | 25.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~72 tok/s |
Selected Q4_K_M
4-bit medium quantization13.5GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 8.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 10.5 GB free · ~109 tok/s
≥50 tok/s needs ≥462 GB/s · RTX 5090 ~194, RTX 3090 Ti ~109
Which Hardware Can Run Qwen1.5 7B?
Q4_K_M · 6.0 GBQwen1.5 7B (Q4_K_M) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 16.1 GB, bringing total usage to 22.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Qwen1.5 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
16–18 GB
16–18 GB tier →fits · 2.5 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~104Simulate output on RTX 5080RTX 5070 Ti~97Simulate output on RTX 5070 TiRTX 4080 SUPER~80Simulate output on RTX 4080 SUPERM516 GB~18Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~18Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~18Simulate output on iPad Pro M5 13" (16 GB)Orin NX~11Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 6.5 GB free · up to 16K ctx
1 GPU
RX 7900 XT~80Simulate output on RX 7900 XT32–40 GB
fits · 18.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~194Simulate output on RTX 5090M5 Max36 GB~54Simulate output on Mac Studio M5 Max (36 GB)M532 GB~18Simulate output on MacBook Air 13" M5 (32 GB)Orin~22Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Qwen1.5 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
Epoch AI · Feb 2024Lab-reported
Lab-reported62.6%
#42/85 open
Alternatives to Qwen1.5 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
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-01-22
- License
- Other
Frequently Asked Questions
How much VRAM does Qwen1.5 7B need?
Qwen1.5 7B requires 6.0 GB of VRAM at Q4_K_M, or 16.8 GB at BF16. Full 33K context adds up to 16.1 GB (22.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.7B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 1.4 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 ≈ 17.5 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Qwen1.5 7B?
For Qwen1.5 7B, Q4_K_M (6.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen1.5 7B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen1.5 7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen1.5 7B locally?
Yes — Qwen1.5 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen1.5 7B?
At Q4_K_M, Qwen1.5 7B can reach ~799 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~109 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 ÷ 6.01 × 0.65 = ~865 tok/s
Estimated speed at Q4_K_M (6.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen1.5 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 Qwen1.5 7B?
52 consumer GPUs can run Qwen1.5 7B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwen1.5 7B?
151 devices with unified memory can run Qwen1.5 7B at Q4_K_M (6.0 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.