Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 marca 2025
Yes, comfortably — you'll have ~4.2 GB of headroom running Gemma 3 4B Instruct at Q4_K_M (3.8 GB, ~72 tok/s (est.)) with room for up to 32K context.
Sprawdź cenę na Amazon — NVIDIA GeForce RTX 4060 8GB
| VRAM | 8 GB |
| Memory Bandwidth | 272 GB/s |
| Quant | VRAM needed | Fits 8 GB? | Max context |
|---|---|---|---|
| F16 | 9.4 GB | ✗ No | — |
| Q8_0 | 5.6 GB | ✓ Yes | 16K |
| Q6_K | 4.7 GB | ✓ Yes | 16K |
| Q5_K_M | 4.2 GB | ✓ Yes | 16K |
| Q4_K_M | 3.8 GB | ✓ Yes | 32K |
| Q3_K_M | 3.1 GB | ✓ Yes | 32K |
| Q2_K | 2.7 GB | ✓ Yes | 32K |
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.
| Gemma 3 4B Instruct | Q4_K_M · 3.8 GB · ~72 tok/s (est.) |
| Gemma 3 1B Instruct | Q4_K_M · 0.9 GB · ~302 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 ~4.2 GB of headroom running Gemma 3 4B Instruct at Q4_K_M (3.8 GB, ~72 tok/s (est.)) with room for up to 32K context.
Gemma 3 4B Instruct at Q4_K_M quantization (3.8 GB), estimated ~72 tokens/sec, up to 32K context.
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