SmolLM2 1.7B Instruct GGUF — Hardware Requirements & GPU Compatibility
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25 downloads
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- Publisher
- MaziyarPanahi
- Parameters
- 1.7B
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HuggingFace
How Much VRAM Does SmolLM2 1.7B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | — | 0.72 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.9 GB | — | 0.83 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 1.0 GB | — | 0.87 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 1.1 GB | — | 0.96 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.1 GB | — | 1.02 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 1.3 GB | — | 1.17 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.3 GB | — | 1.21 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.5 GB | — | 1.40 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | — | 1.70 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM2 1.7B Instruct GGUF?
Q4_K_M · 1.1 GBSmolLM2 1.7B Instruct GGUF (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 SmolLM2 1.7B Instruct GGUF?
Q4_K_M · 1.1 GB33 devices with unified memory can run SmolLM2 1.7B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does SmolLM2 1.7B Instruct GGUF need?
SmolLM2 1.7B Instruct GGUF requires 1.1 GB of VRAM at Q4_K_M, or 1.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.1 GB- What's the best quantization for SmolLM2 1.7B Instruct GGUF?
For SmolLM2 1.7B Instruct GGUF, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
Q2_K0.8 GB~75%Q3_K_L1.0 GB~86%Q4_K_M ★1.1 GB~89%Q5_K_S1.3 GB~92%Q5_K_M1.3 GB~92%Q8_01.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run SmolLM2 1.7B Instruct GGUF on a Mac?
SmolLM2 1.7B Instruct GGUF requires at least 0.8 GB at Q2_K, 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 Instruct GGUF locally?
Yes — SmolLM2 1.7B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 1.7B Instruct GGUF?
At Q4_K_M, SmolLM2 1.7B Instruct GGUF can reach ~2603 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~585 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 ÷ 1.1 × 0.55 = ~2603 tok/s
Estimated speed at Q4_K_M (1.1 GB)
AMD Instinct MI300X~2603 tok/sNVIDIA GeForce RTX 4090~585 tok/sNVIDIA H100 SXM~1945 tok/sAMD Instinct MI250X~1609 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 Instruct GGUF?
At Q4_K_M, the download is about 1.02 GB. The full-precision Q8_0 version is 1.70 GB. The smallest option (Q2_K) is 0.72 GB.
- Which GPUs can run SmolLM2 1.7B Instruct GGUF?
35 consumer GPUs can run SmolLM2 1.7B Instruct GGUF at Q4_K_M (1.1 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 SmolLM2 1.7B Instruct GGUF?
33 devices with unified memory can run SmolLM2 1.7B Instruct GGUF at Q4_K_M (1.1 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.