SmolVLM2 2.2B Instruct — Hardware Requirements & GPU Compatibility
Vision
SmolVLM2-2.2B Instruct is Hugging Face's 2.2-billion-parameter vision-language model, the largest member of the SmolVLM2 family, built to process interleaved text, images, and video within a single conversation. Beyond captioning and visual question answering, it is trained on additional video-instruction data for tasks like summarizing clips or answering questions about video content. At this size it still runs on a single consumer GPU, useful for on-device, resource-constrained deployment. The model supports a context window of 8,192 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in February 2025 alongside smaller 500M and 256M SmolVLM2 siblings. Unlike those variants, which favor raw efficiency, the 2.2B model is tuned to be the most capable of the three on both image and video benchmarks.
139.3K downloads
335 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 2.2B
- Architecture
- SmolVLMForConditionalGeneration
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,280
- Release Date
- 2025-02-08
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does SmolVLM2 2.2B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.1 GB | — | 0.95 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.1 GB | — | 0.98 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.2 GB | — | 1.10 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.2 GB | — | 1.12 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.5 GB | — | 1.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.8 GB | — | 1.60 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.0 GB | — | 1.85 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.5 GB | — | 2.25 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run SmolVLM2 2.2B Instruct?
Q4_K_M · 1.5 GBSmolVLM2 2.2B Instruct (Q4_K_M) requires 1.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolVLM2 2.2B Instruct?
Q4_K_M · 1.5 GB59 devices with unified memory can run SmolVLM2 2.2B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolVLM2 2.2B 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 SmolVLM2 2.2B Instruct need?
SmolVLM2 2.2B Instruct requires 1.5 GB of VRAM at Q4_K_M, or 4.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.2B × 4.8 bits ÷ 8 = 1.3 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.5 GB- What's the best quantization for SmolVLM2 2.2B Instruct?
For SmolVLM2 2.2B Instruct, Q4_K_M (1.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.7 GB.
VRAM requirement by quantization
IQ2_XXS0.7 GBIQ3_XS1.0 GBQ4_01.2 GBQ4_K_M ★1.5 GBQ5_01.5 GBBF164.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolVLM2 2.2B Instruct on a Mac?
SmolVLM2 2.2B Instruct requires at least 0.7 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 SmolVLM2 2.2B Instruct locally?
Yes — SmolVLM2 2.2B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 1.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolVLM2 2.2B Instruct?
At Q4_K_M, SmolVLM2 2.2B Instruct can reach ~3243 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~443 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 ÷ 1.5 × 0.65 = ~3514 tok/s
Estimated speed at Q4_K_M (1.5 GB)
~3514 tok/s~443 tok/s~3514 tok/s~3243 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolVLM2 2.2B Instruct?
At Q4_K_M, the download is about 1.35 GB. The full-precision BF16 version is 4.49 GB. The smallest option (IQ2_XXS) is 0.62 GB.
- Which GPUs can run SmolVLM2 2.2B Instruct?
52 consumer GPUs can run SmolVLM2 2.2B Instruct at Q4_K_M (1.5 GB). 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 SmolVLM2 2.2B Instruct?
59 devices with unified memory can run SmolVLM2 2.2B Instruct at Q4_K_M (1.5 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.