Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, comfortably — you'll have ~7 GB of headroom running SmolLM2 1.7B Instruct at Q4_K_M (1.0324125 GB, ~105 tok/s (est.)) with room for up to 8K context.
| VRAM | 8 GB |
| Memory Bandwidth | 272 GB/s |
| Quant | VRAM needed | Fits 8 GB? | Max context |
|---|---|---|---|
| F16 | 5 GB | ✓ Yes | 8K |
| Q8_0 | 3.4 GB | ✓ Yes | 8K |
| Q6_K | 3 GB | ✓ Yes | 8K |
| Q5_K_M | 2.8 GB | ✓ Yes | 8K |
| Q4_K_M | 2.6 GB | ✓ Yes | 8K |
| Q3_K_M | 2.3 GB | ✓ Yes | 8K |
| Q2_K | 2.2 GB | ✓ Yes | 8K |
VRAM needed assumes a 4K-token context with an f16 KV cache; “Max context” is the largest window that still fits in 8 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
| SmolLM2 1.7B Instruct | Q4_K_M · 1.0324125 GB · ~105 tok/s (est.) |
| SmolLM2 360M Instruct | Q4_K_M · 0.2185575 GB · ~265 tok/s (est.) |
At 2 hrs/day, buying (~$299) beats renting at $0.34/hr after about 15 months.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
Yes, comfortably — you'll have ~7 GB of headroom running SmolLM2 1.7B Instruct at Q4_K_M (1.0324125 GB, ~105 tok/s (est.)) with room for up to 8K context.
SmolLM2 1.7B Instruct at Q4_K_M quantization (1.0324125 GB), estimated ~105 tokens/sec, up to 8K context.
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