SmolLM2 1.7B — Hardware Requirements & GPU Compatibility
Chat
SmolLM2 1.7B is the base pretrained model from Hugging Face's second-generation SmolLM family. Unlike the instruct variant, this model has not been fine-tuned for chat or instruction following, making it a strong foundation for custom fine-tuning, domain adaptation, or research into small-scale language model behavior. At 1.7 billion parameters, it provides meaningful language understanding and generation capabilities while remaining lightweight enough to train and experiment with on consumer hardware. Researchers and developers who want full control over downstream behavior will find this base model more flexible than the instruction-tuned version.
97.2K downloads
145 likesSpecifications
- Publisher
- Hugging Face
- Parameters
- 1.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2025-02-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does SmolLM2 1.7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_XS | 3.30 | 1.4 GB | 2.6 GB | 0.71 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 1.4 GB | 2.6 GB | 0.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.4 GB | 2.7 GB | 0.75 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 1.5 GB | 2.7 GB | 0.77 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 1.5 GB | 2.7 GB | 0.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.6 GB | 2.8 GB | 0.86 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 1.6 GB | 2.8 GB | 0.88 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 1.6 GB | 2.8 GB | 0.92 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 1.7 GB | 2.9 GB | 0.96 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.7 GB | 2.9 GB | 1.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 1.8 GB | 3.0 GB | 1.05 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 1.9 GB | 3.1 GB | 1.18 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.9 GB | 3.1 GB | 1.22 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 1.9 GB | 3.1 GB | 1.24 GB | 5-bit large quantization |
| Q6_K | 6.60 | 2.1 GB | 3.3 GB | 1.41 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.4 GB | 3.6 GB | 1.71 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM2 1.7B?
Q4_K_M · 1.7 GBSmolLM2 1.7B (Q4_K_M) requires 1.7 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 8K context window can add up to 1.2 GB, bringing total usage to 2.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run SmolLM2 1.7B?
Q4_K_M · 1.7 GB33 devices with unified memory can run SmolLM2 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 SmolLM2 1.7B need?
SmolLM2 1.7B requires 1.7 GB of VRAM at Q4_K_M, or 2.4 GB at Q8_0. Full 8K context adds up to 1.2 GB (2.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.9 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M1.7 GBQ4_K_M + full context2.9 GB- What's the best quantization for SmolLM2 1.7B?
For SmolLM2 1.7B, Q4_K_M (1.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (1.8 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 1.4 GB.
VRAM requirement by quantization
IQ3_XS1.4 GB~73%Q3_K_M1.5 GB~83%Q4_K_S1.7 GB~88%Q4_K_M ★1.7 GB~89%Q5_K_S1.9 GB~92%Q8_02.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 1.7B on a Mac?
SmolLM2 1.7B requires at least 1.4 GB at IQ3_XS, 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 SmolLM2 1.7B locally?
Yes — SmolLM2 1.7B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 1.7B?
At Q4_K_M, SmolLM2 1.7B can reach ~1685 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~379 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 ÷ 1.7 × 0.55 = ~1685 tok/s
Estimated speed at Q4_K_M (1.7 GB)
AMD Instinct MI300X~1685 tok/sNVIDIA GeForce RTX 4090~379 tok/sNVIDIA H100 SXM~1259 tok/sAMD Instinct MI250X~1042 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of SmolLM2 1.7B?
At Q4_K_M, the download is about 1.03 GB. The full-precision Q8_0 version is 1.71 GB. The smallest option (IQ3_XS) is 0.71 GB.