DeepSeek R1 Distill 8B needs ~6.9 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q2_K quantization, expect ~25 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
2.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
11.4 tok/s
TTFT
16912 ms
Safe context
4K
Memory
8.6 GB / 6.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 14.9 tok/s | 7087 ms | 4K |
| Coding | F | Too heavy | 11.4 tok/s | 16912 ms | 4K |
| Agentic Coding | F | Too heavy | 7.3 tok/s | 38497 ms | 4K |
| Reasoning | F | Too heavy | 11.4 tok/s | 19987 ms | 4K |
| RAG | F | Too heavy | 7.3 tok/s | 48122 ms | 4K |
How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.1 GB | Low | A70 |
Q3_K_S | 3 | 3.9 GB | Low | F0 |
NVFP4 | 4 | 4.5 GB | Medium | F0 |
Q4_K_M | 4 | 4.9 GB | Medium | F0 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.
Run
ollama run deepseek-r1:8bアップグレードオプション
RTX 3050 8GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 74%.
〜$249 MSRP
RTX 5060 8GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 230%.
〜$299 MSRP
RTX 3060 12GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$329 MSRP
Yes, GTX 1660 Ti 6GB can run DeepSeek R1 Distill 8B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 8.6 GB which exceeds available memory, but at Q2_K it needs only 6.9 GB. Expected decode speed: 25.1 tok/s.
DeepSeek R1 Distill 8B (8B parameters) requires approximately 8.6 GB at Q4_K_M quantization. On GTX 1660 Ti 6GB, it fits at Q2_K using 6.9 GB.
The recommended quantization is Q4_K_M, but on GTX 1660 Ti 6GB the best fitting quantization is Q2_K, which uses 6.9 GB.
On GTX 1660 Ti 6GB, DeepSeek R1 Distill 8B achieves approximately 25.1 tokens per second decode speed with a time-to-first-token of 7710ms using Q2_K quantization.
For coding workloads, DeepSeek R1 Distill 8B on GTX 1660 Ti 6GB receives a F grade with 11.4 tok/s and 4K context.
On GTX 1660 Ti 6GB, DeepSeek R1 Distill 8B can safely use up to 9K tokens of context at Q2_K quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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