SmolLM 1.7B — Hardware Requirements & GPU Compatibility
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SmolLM 1.7B is the largest model in Hugging Face's first-generation SmolLM family. At 1.7 billion parameters, it delivers solid general-purpose text generation in a compact package that runs easily on entry-level hardware, though it has been superseded by the improved SmolLM2 and SmolLM3 series. This model remains a reasonable choice for applications where proven stability matters more than cutting-edge performance. For most new projects, however, users should consider the SmolLM2 1.7B or SmolLM3 3B models, which offer better quality at comparable or only slightly higher resource requirements.
64.3K downloads
181 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 1.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-10-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM 1.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 4.1 GB | — | 3.42 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run SmolLM 1.7B?
BF16 · 4.1 GBSmolLM 1.7B (BF16) requires 4.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM 1.7B?
BF16 · 4.1 GB33 devices with unified memory can run SmolLM 1.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does SmolLM 1.7B need?
SmolLM 1.7B requires 4.1 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 16 bits ÷ 8 = 3.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF164.1 GB- Can I run SmolLM 1.7B on a Mac?
SmolLM 1.7B requires at least 4.1 GB at BF16, 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 SmolLM 1.7B locally?
Yes — SmolLM 1.7B can run locally on consumer hardware. At BF16 quantization it needs 4.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM 1.7B?
At BF16, SmolLM 1.7B can reach ~706 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~159 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 ÷ 4.1 × 0.55 = ~706 tok/s
Estimated speed at BF16 (4.1 GB)
~706 tok/s~159 tok/s~528 tok/s~436 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM 1.7B?
At BF16, the download is about 3.42 GB.
- Which GPUs can run SmolLM 1.7B?
35 consumer GPUs can run SmolLM 1.7B at BF16 (4.1 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 SmolLM 1.7B?
33 devices with unified memory can run SmolLM 1.7B at BF16 (4.1 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.