AlibabaQwen 2.5Qwen2_5_VLForConditionalGeneration

Qwen2.5 VL 7B Instruct — Hardware Requirements & GPU Compatibility

Vision

Qwen2.5 VL 7B Instruct is Alibaba's 8.3-billion-parameter vision-language model in the Qwen 2.5 lineup, built to handle images and video alongside text in a single pass. It can read documents, charts, and screenshots, describe and reason about visual content, and point out object locations within an image, useful for document understanding, visual question answering, and lightweight visual-agent tasks. At this parameter count, local inference is practical on a single mainstream or high-end consumer GPU once the weights are quantized. The model supports a 128K token context window, enough for long documents or extended multi-turn visual conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, and was published in January 2025 as part of Qwen's second-generation vision-language line, later succeeded by Qwen3-VL.

8.3B params · dense128K contextApache 2.0Jan 20256.1M downloads1.8K likes3.4M quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

S

Runs great on a 24 GB GPU

Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~122 tok/s · 125K)Macs from 8 GB (IQ3_XXS · ~20 tok/s)

Apache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Qwen2.5 VL 7B Instruct

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

one command · needs Ollama installed

$ollama run hf.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M

Q4_K_M fits fully on the RTX 4090 · 17.8 GB free · 16K context

Download: Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf4.68 GBunsloth

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M

Generation speed: ~110 tok/s; generation speed
Cost per 1M output tokens: $0.31; per 1M output tokens

Deployon Vast.aiBest valuefrom $0.12/hr

Compare GPUs →

or

How Much VRAM Does Qwen2.5 VL 7B Instruct Need?

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
	2.7 GB	4.5 GB	—	2.4 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Fits: 125K context · ~243 tok/s
	2.9 GB	4.7 GB	—	2.5 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Fits: 125K context · ~225 tok/s
	3.0 GB	4.8 GB	—	2.6 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Fits: 125K context · ~218 tok/s
	3.2 GB	5 GB	—	2.9 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Fits: 125K context · ~204 tok/s
	3.6 GB	5.4 GB	—	3.2 GB	3-bit · i-quant — Importance-weighted 3-bit	Fits: 125K context · ~181 tok/s
	3.7 GB	5.5 GB	—	2.8 GB	2-bit · small K-quant — 2-bit small K-quant	Fits: 125K context · ~176 tok/s
	3.8 GB	5.6 GB	—	3.3 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Fits: 125K context · ~171 tok/s
	3.9 GB	5.7 GB	—	3.5 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Fits: 125K context · ~166 tok/s
	3.9 GB	5.7 GB	—	3 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Fits: 125K context · ~166 tok/s
	4.0 GB	5.8 GB	—	3.5 GB	3-bit small — 3-bit small quantization	Fits: 125K context · ~162 tok/s
	4.2 GB	5.9 GB	—	3.6 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Fits: 125K context · ~158 tok/s
	4.5 GB	6.2 GB	—	3.8 GB	3-bit medium — 3-bit medium quantization	Fits: 125K context · ~147 tok/s
	4.6 GB	6.3 GB	—	4.4 GB	4-bit · legacy — 4-bit legacy quantization	Fits: 125K context · ~144 tok/s
	4.7 GB	6.4 GB	—	4.1 GB	3-bit large — 3-bit large quantization	Fits: 125K context · ~140 tok/s
	4.9 GB	6.6 GB	—	4.2 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Fits: 125K context · ~135 tok/s
	5.1 GB	6.8 GB	—	4.9 GB	4-bit · legacy+ — 4-bit legacy quantization with offset	Fits: 125K context · ~129 tok/s
	5.1 GB	6.8 GB	—	4.5 GB	4-bit small — 4-bit small quantization	Fits: 125K context · ~129 tok/s
	5.1 GB	6.8 GB	—	4.4 GB	4-bit · i-quant non-linear — Importance-weighted 4-bit, non-linear	Fits: 125K context · ~129 tok/s
	5.4 GB	7.2 GB	—	4.7 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Fits: 125K context · ~122 tok/s
	5.5 GB	7.3 GB	—	5.1 GB	4-bit large — 4-bit large quantization	Fits: 125K context · ~119 tok/s
	5.6 GB	7.4 GB	—	5.3 GB	5-bit · legacy — 5-bit legacy quantization	Fits: 125K context · ~117 tok/s
	6.1 GB	7.9 GB	—	5.3 GB	5-bit small — 5-bit small quantization	Fits: 125K context · ~107 tok/s
	6.3 GB	8.1 GB	—	5.4 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Fits: 125K context · ~104 tok/s
	6.4 GB	8.2 GB	—	5.8 GB	5-bit large — 5-bit large quantization	Fits: 125K context · ~102 tok/s
	7.3 GB	9 GB	—	6.3 GB	6-bit, very good — 6-bit quantization, very good quality	Fits: 125K context · ~90 tok/s
	8.7 GB	10.5 GB	—	8.1 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Fits: 125K context · ~75 tok/s
	17 GB	18.8 GB	—	15.2 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Fits: 64K context · ~39 tok/s

Selected Q4_K_M

4-bit medium quantization

6.2GBat 16K context

Weights: 5.0 GB
KV cache: 0.9 GB
Overhead: 0.3 GB

Fits RTX 4090 (reference) · 17.8 GB free · ~122 tok/s

≥50 tok/s needs ≥414 GB/s · RTX 5090 ~216, RTX 3090 Ti ~122

Which Hardware Can Run Qwen2.5 VL 7B Instruct?

Q4_K_M · 5.4 GB

Qwen2.5 VL 7B Instruct (Q4_K_M) requires 5.4 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 128K context window can add up to 7.2 GB, bringing total usage to 12.6 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 Qwen2.5 VL 7B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8 GB · RTX 4060Q4_K_M · 6.2 GB · ~33 tok/s · 32K6.2~3332K8 GB tier →

12 GB · RTX 3060 12GBQ4_K_M · 6.2 GB · ~43 tok/s · 64K6.2~4364K12 GB tier →

16 GB · RTX 5060 Ti 16GBQ4_K_M · 6.2 GB · ~54 tok/s · 125K6.2~54125K16 GB tier →

24 GB · RTX 4090referenceQ4_K_M · 6.2 GB · ~122 tok/s · 125K6.2~122125K24 GB tier →

Mac 32 GB · Mac Mini M4Q4_K_M · 6.2 GB · ~16 tok/s · 125K6.2~16125KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 6.2 GB · ~71 tok/s · 125K6.2~71125KMacs →

Alternatives to Qwen2.5 VL 7B Instruct

Qwen 2.5 family · 44 models →

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
Granite Switch 4.1 8BIBM	—	8.4 GB	~111	same size
ALoDLM 8BAmazon	—	7.6 GB	~119	same size
Llm Jp 4.1 8B Thinkingllm-jp	—	7.6 GB	~115	same size
Ling 3.0 TinyInclusion AI	—	5.5 GB	~364	same size
Apertus V1.5 8Bswiss-ai	—	8.5 GB	~111	same size
LFM2.5 8B A1BLiquid AI	—	5.6 GB	~335	same size

Fine-tunes & variants(1)community builds of Qwen2.5 VL 7B Instruct

OlmOCR 2 7B 1025

Specifications

Publisher: Alibaba
Family: Qwen 2.5
Parameters: 8.3B
Architecture: Qwen2_5_VLForConditionalGeneration
Context Length: 128,000 tokens
Vocabulary Size: 152,064
Release Date: 2025-01-26
License: Apache 2.0

Resources

Hugging Face model card (opens in a new tab)
Qwen 2.5 family

Frequently Asked Questions

How much VRAM does Qwen2.5 VL 7B Instruct need?

Qwen2.5 VL 7B Instruct requires 5.4 GB of VRAM at Q4_K_M, or 17 GB at BF16. Full 128K context adds up to 7.2 GB (12.6 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 8.3B × 4.8 bits ÷ 8 = 5 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 ≈ 7.6 GB (at full 128K context)

VRAM usage by quantization

Q4_K_M

5.4 GB

Q4_K_M + full context

12.6 GB

Learn more about VRAM estimation →

What's the best quantization for Qwen2.5 VL 7B Instruct?

For Qwen2.5 VL 7B Instruct, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.7 GB.

VRAM requirement by quantization

IQ2_XXS

2.7 GB~53%

IQ3_S

3.9 GB~75%

Q3_K_L

4.7 GB~86%

Q4_K_M ★

5.4 GB~89%

Q5_0

5.6 GB~90%

BF16

17.0 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen2.5 VL 7B Instruct on a Mac?

Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen2.5 VL 7B Instruct, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).

Can I run Qwen2.5 VL 7B Instruct locally?

Yes — Qwen2.5 VL 7B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen2.5 VL 7B Instruct?

At Q4_K_M, Qwen2.5 VL 7B Instruct can reach ~891 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 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 ÷ 5.39 × 0.65 = ~965 tok/s

Estimated speed at Q4_K_M (5.4 GB)

NVIDIA B200

~965 tok/s

NVIDIA GeForce RTX 4090

~122 tok/s

NVIDIA B300

~965 tok/s

AMD Instinct MI350X

~891 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Qwen2.5 VL 7B Instruct?

At Q4_K_M, the download is about 4.98 GB. The full-precision BF16 version is 16.58 GB. The smallest option (IQ2_XXS) is 2.28 GB.

Which GPUs can run Qwen2.5 VL 7B Instruct?

52 consumer GPUs can run Qwen2.5 VL 7B Instruct 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 Qwen2.5 VL 7B Instruct?

151 devices with unified memory can run Qwen2.5 VL 7B Instruct at Q4_K_M (5.4 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.

Run Qwen2.5 VL 7B Instruct

6.2 GB VRAM at 16K context · fits the RTX 4090 (17.8 GB free)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and search qwen/qwen2.5-vl-7b, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get qwen/qwen2.5-vl-7b@q4_k_m
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve Qwen/Qwen2.5-VL-7B-Instruct --max-model-len 16384
Runs the original weights, not the Q4_K_M GGUF; needs ~18 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$unsloth/Qwen2.5-VL-7B-Instruct-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
RTX 3080 Ti (12 GB)CheapestCheapest5 GB free	~110 tok/s	$0.31	Deploy on Vast.aifrom $0.12/hr (opens in new tab)	12 GB · 5 GB free · ~110 tok/s · $0.31 / 1M tokens
RTX 3060 12GB5 GB free	~43 tok/s	$0.38	Deploy on Vast.aifrom $0.060/hr (opens in new tab)	5 GB free · ~43 tok/s · $0.38 / 1M tokens
RTX 3060 Ti (8 GB)1 GB free	~54 tok/s	$0.35	Deploy on Vast.aifrom $0.069/hr (opens in new tab)	8 GB · 1 GB free · ~54 tok/s · $0.35 / 1M tokens
RTX 4060 (8 GB)1 GB free	~33 tok/s	$0.73	Deploy on Vast.aifrom $0.086/hr (opens in new tab)	8 GB · 1 GB free · ~33 tok/s · $0.73 / 1M tokens
RTX A4000 (16 GB)9 GB free	~54 tok/s	$0.45	Deploy on Vast.aifrom $0.088/hr (opens in new tab)	16 GB · 9 GB free · ~54 tok/s · $0.45 / 1M tokens
RTX A5000 (24 GB)17 GB free	~93 tok/s	$0.48	Deploy on RunPodfrom $0.16/hr (opens in new tab)	24 GB · 17 GB free · ~93 tok/s · $0.48 / 1M tokens
RTX 4000 Ada (20 GB)13 GB free	~43 tok/s	$1.28	Deploy on RunPodfrom $0.20/hr (opens in new tab)	20 GB · 13 GB free · ~43 tok/s · $1.28 / 1M tokens
RTX 4090 (24 GB)17 GB free	~122 tok/s	$0.78	Deploy on RunPodfrom $0.34/hr (opens in new tab)	24 GB · 17 GB free · ~122 tok/s · $0.78 / 1M tokens

Compare GPUs →