Home / Models / SmolLM2 1.7B Instruct / NVIDIA GTX 1060 6 GB
Can SmolLM2 1.7B Instruct run on NVIDIA GTX 1060 6 GB?
Yes. Calculated memory fit: SmolLM2 1.7B Instruct can run entirely in this GPU’s usable VRAM at one or more supported quantizations.
Can it run?
- Can it run?
- Yes, full GPU fit (calculated estimate).
- Full GPU fit?
- Yes
- Model
- SmolLM2 1.7B Instruct · 1.7B
- GPU
- NVIDIA GTX 1060 6 GB · 6 GB advertised
What else can NVIDIA GTX 1060 6 GB run? · Find other GPUs for SmolLM2 1.7B Instruct
Memory calculation
Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.
| Model weights | 1.0 GB |
|---|---|
| KV cache | 1.5 GB |
| Runtime reserve | 0.6 GB |
| Safety margin | 0.3 GB |
| Required (estimated) | 3.3 GB |
| GPU usable VRAM | 5.4 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K |
|---|---|---|---|
| BF16 | Fits | Fits | Limited context |
| FP16 | Fits | Fits | Limited context |
| Q3 | Fits | Fits | Fits |
| Q4 | Fits | Fits | Fits |
| Q5 | Fits | Fits | Fits |
| Q6 | Fits | Fits | Fits |
| Q8 | Fits | Fits | Fits |
What fits entirely in VRAM?
- BF16 at 2K context
- BF16 at 4K context
- FP16 at 2K context
- FP16 at 4K context
- Q3 at 2K context
- Q3 at 4K context
- Q3 at 8K context
- Q4 at 2K context
- Q4 at 4K context
- Q4 at 8K context
- Q5 at 2K context
- Q5 at 4K context
What requires RAM offload?
Offload is shown only when the model does not fully fit in usable VRAM but stays within the modeled offload allowance.
Performance
Separate from memory fit. Runtime is llama.cpp · quantization bucket Q4 (GGUF Q4_0 maps to Q4). Workload is llama-bench pp512 / tg128 (short context, batch 1, full GPU offload). This is not a ranking and not a 8K–128K speed claim.
- Model
- SmolLM2 1.7B Instruct
- GPU
- NVIDIA GTX 1060 6 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~100 tok/s · Calibrated estimate · MEDIUM confidence (spread ~88 tok/s–~112 tok/s)
- Prefill
- ~1759 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
- Time to first token
- ~291 ms for a 512-token prompt (512 / prefill tok/s). Not estimated at 8K–128K.
decode_tps = GPU_efficiency × memory_bandwidth / model_weight_bytes; efficiency from same-gpu Llama 2 7B Q4_0 efficiency
Available graphics cards
These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.
ASUS DUAL-GTX1060-O6G NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon EXACT
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GIGABYTE Windforce NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon EXACT
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GIGABYTE GV-N1060IXOC-6GD graphics card NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon EXACT
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GIGABYTE GAMING GV-N1060G1 -6GD graphics card NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon EXACT
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ASUS TURBO-GTX1060-6G NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon EXACT
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ASUS DUAL-GTX1060-6G NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon unmatched
ASUS ROG STRIX-GTX1060-6G-GAMING NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon unmatched
ASUS ROG STRIX-GTX1060-O6G-GAMING NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon unmatched
ASUS ROG STRIX-GTX1060-O6G-GAMING NVIDIA GeForce GTX 1060 6 GB GDDR5
6 GB · Amazon unmatched
GIGABYTE GAMING GeForce GTX 1060 G1 6G (rev. 2.0) NVIDIA 6 GB GDDR5
6 GB · Amazon unmatched
GIGABYTE GeForce GTX 1060 WINDFORCE 6G NVIDIA 6 GB GDDR5
6 GB · Amazon unmatched
Sources
Model: official config HuggingFaceTB/SmolLM2-1.7B-Instruct (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: FITS_IN_VRAM. Amazon: affiliate commerce match, not a spec source.