SmolVLM 256M Instruct — Hardware Requirements & GPU Compatibility
Vision
SmolVLM-256M Instruct is Hugging Face's 256-million-parameter vision-language model, built on a compact SmolLM2-135M language backbone paired with a small 93-million-parameter SigLIP vision encoder. Hugging Face calls it the smallest publicly available multimodal model, designed for image captioning, visual question answering, and basic text transcription from images rather than open-ended chat. Its size makes it practical to run even on CPUs or entry-level GPUs, and on-device deployment is a stated use case. Context length is limited to 8,192 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in January 2025. Compared to the larger 2.2B SmolVLM2 sibling, it uses a smaller vision encoder and more aggressive image-token compression to further cut memory and latency, at some cost to accuracy.
441.5K downloads
411 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 256M
- Architecture
- Idefics3ForConditionalGeneration
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,280
- Release Date
- 2025-01-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolVLM 256M Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 0.5 GB | 0.6 GB | 0.11 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.5 GB | 0.6 GB | 0.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.5 GB | 0.6 GB | 0.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.5 GB | 0.6 GB | 0.13 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.5 GB | 0.6 GB | 0.15 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.5 GB | 0.7 GB | 0.18 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.6 GB | 0.7 GB | 0.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.6 GB | 0.8 GB | 0.26 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 SmolVLM 256M Instruct?
Q4_K_M · 0.5 GBSmolVLM 256M Instruct (Q4_K_M) requires 0.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 8K context window can add up to 0.1 GB, bringing total usage to 0.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolVLM 256M Instruct?
Q4_K_M · 0.5 GB59 devices with unified memory can run SmolVLM 256M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolVLM 256M Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does SmolVLM 256M Instruct need?
SmolVLM 256M Instruct requires 0.5 GB of VRAM at Q4_K_M, or 0.9 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 256M × 4.8 bits ÷ 8 = 0.2 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.4 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.5 GBQ4_K_M + full context0.6 GB- What's the best quantization for SmolVLM 256M Instruct?
For SmolVLM 256M Instruct, Q4_K_M (0.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.4 GB.
VRAM requirement by quantization
IQ2_XXS0.4 GBIQ3_XS0.5 GBQ4_00.5 GBIQ4_NL0.5 GBQ4_K_M ★0.5 GBBF160.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolVLM 256M Instruct on a Mac?
SmolVLM 256M Instruct requires at least 0.4 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 SmolVLM 256M Instruct locally?
Yes — SmolVLM 256M Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 0.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolVLM 256M Instruct?
At Q4_K_M, SmolVLM 256M Instruct can reach ~9600 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1310 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 ÷ 0.5 × 0.65 = ~10400 tok/s
Estimated speed at Q4_K_M (0.5 GB)
~10400 tok/s~1310 tok/s~10400 tok/s~9600 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolVLM 256M Instruct?
At Q4_K_M, the download is about 0.15 GB. The full-precision BF16 version is 0.51 GB. The smallest option (IQ2_XXS) is 0.07 GB.
- Which GPUs can run SmolVLM 256M Instruct?
52 consumer GPUs can run SmolVLM 256M Instruct at Q4_K_M (0.5 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 SmolVLM 256M Instruct?
59 devices with unified memory can run SmolVLM 256M Instruct at Q4_K_M (0.5 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.