SmolVLM 500M Instruct — Hardware Requirements & GPU Compatibility
Vision
SmolVLM-500M-Instruct is Hugging Face's 500-million-parameter multimodal model in the SmolVLM family, accepting interleaved image and text input and producing text. It targets image captioning, visual question answering, text transcription and storytelling over images, and does not generate images. Built on the Idefics3 architecture with a SmolLM2-360M language model and a SigLIP vision encoder, it encodes each 512x512 image patch with 64 visual tokens to reduce memory use. At this size it runs comfortably on a modest consumer GPU, a laptop or even a CPU, which suits on-device use. The context length is 8,192 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in January 2025. It sits between the 256M and 2.2B SmolVLM models, and the card says it is English-only and can be fine-tuned for specific tasks.
150.9K downloads
198 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 507M
- Architecture
- Idefics3ForConditionalGeneration
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,280
- Release Date
- 2025-01-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolVLM 500M Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.6 GB | 0.8 GB | 0.22 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.6 GB | 0.9 GB | 0.22 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.6 GB | 0.9 GB | 0.25 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.6 GB | 0.9 GB | 0.25 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.7 GB | 0.9 GB | 0.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.8 GB | 1 GB | 0.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.8 GB | 1.1 GB | 0.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.9 GB | 1.1 GB | 0.51 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 500M Instruct?
Q4_K_M · 0.7 GBSmolVLM 500M Instruct (Q4_K_M) requires 0.7 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 0.3 GB, bringing total usage to 0.9 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 500M Instruct?
Q4_K_M · 0.7 GB59 devices with unified memory can run SmolVLM 500M Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolVLM 500M 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 SmolVLM 500M Instruct need?
SmolVLM 500M Instruct requires 0.7 GB of VRAM at Q4_K_M, or 1.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 507M × 4.8 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.6 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M0.7 GBQ4_K_M + full context0.9 GB- What's the best quantization for SmolVLM 500M Instruct?
For SmolVLM 500M Instruct, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.5 GB.
VRAM requirement by quantization
IQ2_XXS0.5 GBIQ3_XS0.6 GBQ4_00.6 GBIQ4_NL0.7 GBQ4_K_M ★0.7 GBBF161.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolVLM 500M Instruct on a Mac?
SmolVLM 500M Instruct requires at least 0.5 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 500M Instruct locally?
Yes — SmolVLM 500M Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolVLM 500M Instruct?
At Q4_K_M, SmolVLM 500M Instruct can reach ~6957 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~950 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 ÷ 0.7 × 0.65 = ~7536 tok/s
Estimated speed at Q4_K_M (0.7 GB)
~7536 tok/s~950 tok/s~7536 tok/s~6957 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolVLM 500M Instruct?
At Q4_K_M, the download is about 0.30 GB. The full-precision BF16 version is 1.01 GB. The smallest option (IQ2_XXS) is 0.14 GB.
- Which GPUs can run SmolVLM 500M Instruct?
52 consumer GPUs can run SmolVLM 500M Instruct at Q4_K_M (0.7 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 500M Instruct?
59 devices with unified memory can run SmolVLM 500M Instruct at Q4_K_M (0.7 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.