SmolLM2 1.7B Instruct Q8 0 GGUF — Hardware Requirements & GPU Compatibility
Chat
24 downloads
1 likesSpecifications
- Publisher
- NikolayKozloff
- Parameters
- 1.7B
- License
- Apache 2.0
Get Started
How Much VRAM Does SmolLM2 1.7B Instruct Q8 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8.00 | 1.9 GB | — | 1.70 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run SmolLM2 1.7B Instruct Q8 0 GGUF?
Q8_0 · 1.9 GBSmolLM2 1.7B Instruct Q8 0 GGUF (Q8_0) requires 1.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ 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 Q8 0 GGUF?
Q8_0 · 1.9 GB33 devices with unified memory can run SmolLM2 1.7B Instruct Q8 0 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 Q8 0 GGUF need?
SmolLM2 1.7B Instruct Q8 0 GGUF requires 1.9 GB of VRAM at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 8 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q8_01.9 GB- Can I run SmolLM2 1.7B Instruct Q8 0 GGUF on a Mac?
SmolLM2 1.7B Instruct Q8 0 GGUF requires at least 1.9 GB at Q8_0, 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 Q8 0 GGUF locally?
Yes — SmolLM2 1.7B Instruct Q8 0 GGUF can run locally on consumer hardware. At Q8_0 quantization it needs 1.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is SmolLM2 1.7B Instruct Q8 0 GGUF?
At Q8_0, SmolLM2 1.7B Instruct Q8 0 GGUF can reach ~1559 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~350 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 ÷ 1.9 × 0.55 = ~1559 tok/s
Estimated speed at Q8_0 (1.9 GB)
AMD Instinct MI300X~1559 tok/sNVIDIA GeForce RTX 4090~350 tok/sNVIDIA H100 SXM~1165 tok/sAMD Instinct MI250X~964 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 Q8 0 GGUF?
At Q8_0, the download is about 1.70 GB.
- Which GPUs can run SmolLM2 1.7B Instruct Q8 0 GGUF?
35 consumer GPUs can run SmolLM2 1.7B Instruct Q8 0 GGUF at Q8_0 (1.9 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 Q8 0 GGUF?
33 devices with unified memory can run SmolLM2 1.7B Instruct Q8 0 GGUF at Q8_0 (1.9 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.