SmolLM2-1.7B-Instruct GGUF size and VRAM requirements
HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF is a compact instruction-tuned chat model with 1.71 billion parameters, built on the llama architecture. It is released under the apache-2.0 license and has been downloaded 15,023 times.
Under the hood it uses 24 transformer layers, a hidden size of 2,048, 32 attention heads.
To run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF locally at a 4,096-token context, its quantized versions need between 2.53 GB (Q4_K_M, lowest quality) and 2.53 GB (Q4_K_M, highest quality) of memory, weights plus KV cache and a system margin included. Context length drives that memory directly: at 4,096 tokens the KV cache for SmolLM2-1.7B-Instruct-GGUF is about 0.75 GB, rising to roughly 1.5 GB at its full 8,192-token context. Shorter prompts free up memory for a higher-quality quantization.
For most users the best balance is Q4_K_M, needing about 2.53 GB. That means HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
Available GGUF quantizations for HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF include Q4_K_M. The model supports a native context length of up to 8,192 tokens; a longer context grows the KV cache and the memory needed.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q4_K_M | 4.93 | Good | 0.98 GB | 0.75 GB | 2.53 GB | 406.9 t/s | Fits in VRAM |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF?
HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF is an instruction-tuned chat model with 1.71 billion parameters, based on the llama architecture. It is released under the apache-2.0 license and distributed as GGUF files for local inference.
How much VRAM do you need to run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF?
You need about 2.53 GB of VRAM to run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF fully on the GPU using Q4_K_M (about 2.53 GB).
Can I run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF fully on the GPU using Q4_K_M (about 2.53 GB).
Can I run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF fully on the GPU using Q4_K_M (about 2.53 GB).
What context length does HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF support?
HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF supports a native context length of up to 8,192 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF?
For HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF, a strong default is Q4_K_M, which needs about 2.53 GB and keeps most of the quality while roughly halving the memory versus 8-bit. With VRAM to spare, Q5_K_M or Q6_K add a little more quality; if you are tight on memory, a smaller quantization still runs. Pick the highest quantization that fits your VRAM.