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solid general-purpose text generation in a compact package that runs easily on entry-level hardware, though it has been superseded by the improved SmolLM2 and SmolLM3 series.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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~379 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~41 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run SmolLM 1.7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/SmolLM-1.7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 22.3 GB free · 2K context
Download: SmolLM-1.7B.Q4_K_M.gguf1.06 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.12
- per 1M output tokens
How Much VRAM Does SmolLM 1.7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~458 tok/s | |
| 1.4 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~452 tok/s | |
| 1.5 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~426 tok/s | |
| 1.6 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~420 tok/s | |
| 1.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~379 tok/s | |
| 1.9 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~341 tok/s | |
| 2.1 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~311 tok/s | |
| 2.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~272 tok/s |
Selected Q4_K_M
4-bit medium quantization1.7GBat 2K context
- Weights
- 1.0 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.3 GB free · ~379 tok/s
≥50 tok/s needs ≥133 GB/s · RTX 5090 ~673, RTX 3090 Ti ~379
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run SmolLM 1.7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.3 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~286Simulate output on RTX 3080RTX 2080 Ti~231Simulate output on RTX 2080 TiRTX 3070 Ti~229Simulate output on RTX 3070 TiM3*8 GB~41Simulate output on iMac 24" M3 (8 GB)Orin Nano~38Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~28Simulate output on iPhone 1720 GB
fits · 18.3 GB free · up to 2K ctx
1 GPU
RX 7900 XT~278Simulate output on RX 7900 XT32–40 GB
fits · 30.3 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~673Simulate output on RTX 5090M5 Max36 GB~186Simulate output on Mac Studio M5 Max (36 GB)M532 GB~62Simulate output on MacBook Air 13" M5 (32 GB)Orin~77Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~51Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is SmolLM 1.7B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent11.9%
#142/146 open
Alternatives to SmolLM 1.7B
SmolLM family · 15 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM3 1.7BLiquid AI | — | 2.1 GB | ~455 | same size |
| Granite 4.0 1BIBM | — | 2.6 GB | ~452 | same size |
| LFM2 VL 1.6BLiquid AI | — | 1.5 GB | ~496 | same size |
| Qwen 1 8BAlibaba | — | 1.9 GB | ~542 | same size |
| JiRackUltra 1BCMSManhattan | — | 1.8 GB | ~461 | same size |
| Qwen3 1.7B BaseAlibaba | — | 3.2 GB | ~417 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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 IQ2_XXS at 1.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run SmolLM 1.7B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run SmolLM 1.7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run SmolLM 1.7B locally?
Yes — SmolLM 1.7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is SmolLM 1.7B?
At Q4_K_M, SmolLM 1.7B can reach ~2775 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 ÷ 1.73 × 0.65 = ~3006 tok/s
Estimated speed at Q4_K_M (1.7 GB)
Real-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 (IQ2_XXS) is 0.47 GB.
Which GPUs can run SmolLM 1.7B?
52 consumer GPUs can run SmolLM 1.7B at Q4_K_M. 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 SmolLM 1.7B?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.