Home / Models / SmolLM2 1.7B Instruct / NVIDIA RTX 4070 Ti SUPER 16 GB
Can SmolLM2 1.7B Instruct run on NVIDIA RTX 4070 Ti SUPER 16 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 RTX 4070 Ti SUPER 16 GB · 16 GB advertised
What else can NVIDIA RTX 4070 Ti SUPER 16 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 | 14.4 GB |
Result: FITS IN VRAM
Quantization table
| Quantization | 2K | 4K | 8K |
|---|---|---|---|
| BF16 | Fits | Fits | Fits |
| FP16 | Fits | Fits | Fits |
| 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
- BF16 at 8K context
- FP16 at 2K context
- FP16 at 4K context
- FP16 at 8K 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
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 RTX 4070 Ti SUPER 16 GB
- Runtime
- llama.cpp · CUDA · model 1.0.0
- Decode
- ~471 tok/s · Calibrated estimate · MEDIUM confidence (spread ~415 tok/s–~528 tok/s)
- Prefill
- ~30004 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
- Time to first token
- ~17 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.
MSI VENTUS GeForce RTX 4070 Ti SUPER 16G 2X OC NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
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GIGABYTE GAMING GeForce RTX 4070 Ti SUPER OC 16G NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
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GIGABYTE EAGLE GeForce RTX 4070 Ti SUPER OC 16G NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
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MSI GeForce RTX 4070 Ti SUPER 16G VENTUS 2X WHITE OC NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
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ASUS TUF Gaming TUF-RTX4070TIS-O16G-GAMING NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 GB · Amazon EXACT
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GIGABYTE GeForce RTX 4070 Ti SUPER WINDFORCE OC 16G NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
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GIGABYTE AERO GeForce RTX 4070 Ti SUPER OC 16G NVIDIA 16 GB GDDR6X
16 GB · Amazon EXACT
Check price on AmazonASUS TUF Gaming TUF-RTX4070TIS-16G-GAMING NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 GB · Amazon unmatched
ASUS ROG -STRIX-RTX4070TIS-O16G-GAMING NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 GB · Amazon unmatched
ASUS TUF Gaming TUF-RTX4070TIS-O16G-WHITE-GAMING NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 GB · Amazon unmatched
ASUS Dual -RTX4070TIS-O16G NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 GB · Amazon unmatched
ASUS ProArt -RTX4070TIS-O16G NVIDIA GeForce RTX 4070 Ti SUPER 16 GB GDDR6X
16 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.