作者： Jakub Rusinowski · 最后更新： 2024年7月23日
Yes, comfortably — you'll have ~9.5 GB of headroom running Llama 3.1 8B Instruct at Q4_K_M (6.5 GB, ~138 tok/s (est.)) with room for up to 64K context.
在亚马逊查看价格 — NVIDIA GeForce RTX 5070 Ti 16GB
| VRAM | 16 GB |
| Memory Bandwidth | 896 GB/s |
| Quant | VRAM needed | Fits 16 GB? | Max context |
|---|---|---|---|
| F16 | 17.3 GB | ✗ No | — |
| Q8_0 | 9.8 GB | ✓ Yes | 32K |
| Q6_K | 7.9 GB | ✓ Yes | 64K |
| Q5_K_M | 7 GB | ✓ Yes | 64K |
| Q4_K_M | 6.2 GB | ✓ Yes | 64K |
| Q3_K_M | 4.7 GB | ✓ Yes | 64K |
| Q2_K | 4 GB | ✓ Yes | 64K |
VRAM needed assumes a 4K-token context with an f16 KV cache; “Max context” is the largest window that still fits in 16 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
| Llama 3.1 8B Instruct | Q4_K_M · 6.5 GB · ~138 tok/s (est.) |
At 2 hrs/day, buying (~$749) beats renting at $0.34/hr after about 3.1 years.
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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 ~9.5 GB of headroom running Llama 3.1 8B Instruct at Q4_K_M (6.5 GB, ~138 tok/s (est.)) with room for up to 64K context.
Llama 3.1 8B Instruct at Q4_K_M quantization (6.5 GB), estimated ~138 tokens/sec, up to 64K context.
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