SmolLM3 3B ONNX — Hardware Requirements & GPU Compatibility
Chat
SmolLM3 3B ONNX is a 3B-parameter open language model from Hugging Face. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 2.25 GB of VRAM — see which GPUs and Macs can run it below.
267 downloads
26 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 3B
- Architecture
- SmolLM3ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM3 3B ONNX Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.7 GB | 6.4 GB | 1.27 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.8 GB | 6.4 GB | 1.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.9 GB | 6.6 GB | 1.46 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.9 GB | 6.6 GB | 1.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.3 GB | 6.9 GB | 1.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.6 GB | 7.3 GB | 2.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.9 GB | 7.6 GB | 2.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 8.1 GB | 3.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM3 3B ONNX?
Q4_K_M · 2.3 GBSmolLM3 3B ONNX (Q4_K_M) requires 2.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 66K context window can add up to 4.7 GB, bringing total usage to 6.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM3 3B ONNX?
Q4_K_M · 2.3 GB33 devices with unified memory can run SmolLM3 3B ONNX, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does SmolLM3 3B ONNX need?
SmolLM3 3B ONNX requires 2.3 GB of VRAM at Q4_K_M, or 3.5 GB at Q8_0. Full 66K context adds up to 4.7 GB (6.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 5.1 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M2.3 GBQ4_K_M + full context6.9 GB- What's the best quantization for SmolLM3 3B ONNX?
For SmolLM3 3B ONNX, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.3 GB.
VRAM requirement by quantization
IQ2_XXS1.3 GBQ3_K_S1.8 GBIQ4_XS2.1 GBQ4_K_M ★2.3 GBQ4_K_L2.3 GBQ8_03.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM3 3B ONNX on a Mac?
SmolLM3 3B ONNX requires at least 1.3 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 SmolLM3 3B ONNX locally?
Yes — SmolLM3 3B ONNX can run locally on consumer hardware. At Q4_K_M quantization it needs 2.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM3 3B ONNX?
At Q4_K_M, SmolLM3 3B ONNX can reach ~1296 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~291 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.3 × 0.55 = ~1296 tok/s
Estimated speed at Q4_K_M (2.3 GB)
~1296 tok/s~291 tok/s~968 tok/s~801 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM3 3B ONNX?
At Q4_K_M, the download is about 1.80 GB. The full-precision Q8_0 version is 3.00 GB. The smallest option (IQ2_XXS) is 0.83 GB.
- Which GPUs can run SmolLM3 3B ONNX?
35 consumer GPUs can run SmolLM3 3B ONNX at Q4_K_M (2.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM3 3B ONNX?
33 devices with unified memory can run SmolLM3 3B ONNX at Q4_K_M (2.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.