Qwen3 VL 8B Instruct — Hardware Requirements & GPU Compatibility
Vision
Qwen3 VL 8B Instruct is Alibaba's 8.8-billion-parameter vision-language model in the Qwen3-VL lineup, built to process images and text together in one conversation. Beyond image description and visual question answering, it is tuned as a visual agent that can read GUI screenshots and reason about on-screen elements, useful for document analysis and early automation tasks. At this size, local inference is practical on a single mainstream-to-high-end consumer GPU once quantized. The model supports a 262,144 token context window, enough for long documents or extended chat histories. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2025 alongside 2B and 32B siblings, Qwen3-VL adds video understanding with fine-grained event indexing beyond earlier Qwen vision-language releases.
20.1M downloads
1.1K likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 8.8B
- Architecture
- Qwen3VLForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-10-11
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 VL 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.3 GB | 42.7 GB | 3.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.4 GB | 42.8 GB | 3.84 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.9 GB | 43.2 GB | 4.27 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.0 GB | 43.3 GB | 4.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.9 GB | 44.2 GB | 5.26 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.8 GB | 45.2 GB | 6.25 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.8 GB | 46.2 GB | 7.23 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.4 GB | 47.7 GB | 8.77 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 VL 8B Instruct?
Q4_K_M · 5.9 GBQwen3 VL 8B Instruct (Q4_K_M) requires 5.9 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 262K context window can add up to 38.4 GB, bringing total usage to 44.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3 VL 8B Instruct?
Q4_K_M · 5.9 GB58 devices with unified memory can run Qwen3 VL 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Qwen3 VL 8B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 VL 8B Instruct need?
Qwen3 VL 8B Instruct requires 5.9 GB of VRAM at Q4_K_M, or 18.1 GB at BF16. Full 262K context adds up to 38.4 GB (44.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 4.8 bits ÷ 8 = 5.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 38.9 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M5.9 GBQ4_K_M + full context44.2 GB- What's the best quantization for Qwen3 VL 8B Instruct?
For Qwen3 VL 8B Instruct, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.0 GB.
VRAM requirement by quantization
IQ2_XXS3.0 GBQ3_K_S4.4 GBIQ4_NL5.5 GBQ4_K_M ★5.9 GBQ5_K_S6.6 GBBF1618.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 VL 8B Instruct on a Mac?
Qwen3 VL 8B Instruct requires at least 3.0 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3 VL 8B Instruct locally?
Yes — Qwen3 VL 8B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 5.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 VL 8B Instruct?
At Q4_K_M, Qwen3 VL 8B Instruct can reach ~819 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~112 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 ÷ 5.9 × 0.65 = ~887 tok/s
Estimated speed at Q4_K_M (5.9 GB)
~887 tok/s~112 tok/s~887 tok/s~819 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 VL 8B Instruct?
At Q4_K_M, the download is about 5.26 GB. The full-precision BF16 version is 17.53 GB. The smallest option (IQ2_XXS) is 2.41 GB.
- Which GPUs can run Qwen3 VL 8B Instruct?
52 consumer GPUs can run Qwen3 VL 8B Instruct at Q4_K_M (5.9 GB). 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 Qwen3 VL 8B Instruct?
59 devices with unified memory can run Qwen3 VL 8B Instruct at Q4_K_M (5.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.