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solid general-purpose text generation in a compact package that runs easily on entry-level hardware, though it has been superseded by the improved SmolLM2 and SmolLM3 series.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
- Family
- SmolLM
- Parameters
- 1.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-07-14
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.4 GB | — | 0.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.4 GB | — | 0.75 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.5 GB | — | 0.83 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 1.7 GB | — | 1.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.9 GB | — | 1.22 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.1 GB | — | 1.41 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.4 GB | — | 1.71 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 SmolLM 1.7B?
Q4_K_M · 1.7 GBSmolLM 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. 50 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?
Q4_K_M · 1.7 GB59 devices with unified memory can run SmolLM 1.7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download SmolLM 1.7B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does SmolLM 1.7B need?
SmolLM 1.7B requires 1.7 GB of VRAM at Q4_K_M, or 4.1 GB at BF16.
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)
VRAM usage by quantization
Q4_K_M1.7 GB- What's the best quantization for SmolLM 1.7B?
For SmolLM 1.7B, Q4_K_M (1.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.9 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 GBIQ3_M1.5 GBIQ4_XS1.6 GBQ4_K_M ★1.7 GBQ5_K_M1.9 GBBF164.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM 1.7B on a Mac?
SmolLM 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 SmolLM 1.7B locally?
Yes — SmolLM 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 SmolLM 1.7B?
At Q4_K_M, SmolLM 1.7B can reach ~2543 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 1.7 × 0.65 = ~3006 tok/s
Estimated speed at Q4_K_M (1.7 GB)
~3006 tok/s~379 tok/s~3006 tok/s~2543 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 Q4_K_M, the download is about 1.03 GB. The full-precision BF16 version is 3.42 GB. The smallest option (IQ3_XS) is 0.71 GB.
- Which GPUs can run SmolLM 1.7B?
50 consumer GPUs can run SmolLM 1.7B at Q4_K_M (1.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run SmolLM 1.7B?
59 devices with unified memory can run SmolLM 1.7B at Q4_K_M (1.7 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.