DeepSeek R1 Distill Qwen 14B needs ~9.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q2_K quantization, expect ~19 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
4.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.6 tok/s
TTFT
25405 ms
Safe context
4K
Memory
12.2 GB / 8.0 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.
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 0.7 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 | 8.8 tok/s | 11966 ms | 4K |
| Coding | F | Too heavy | 7.6 tok/s | 25405 ms | 4K |
| Agentic Coding | F | Too heavy | 5.8 tok/s | 48213 ms | 4K |
| Reasoning | F | Too heavy | 7.6 tok/s | 30024 ms | 4K |
| RAG | F | Too heavy | 5.8 tok/s | 60266 ms | 4K |
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | F0 |
Q3_K_S | 3 | 6.9 GB | Low | F0 |
NVFP4 | 4 | 7.8 GB | Medium | F0 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek R1 Distill Qwen 14B on your machine.
Run
lms load hf-unsloth--deepseek-r1-distill-qwen-14b-gguf && lms server startアップグレードオプション
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
RTX 5060 Ti 16GBコスパ最良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.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレード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.
〜$499 MSRP
Yes, RTX 4060 Ti 8GB can run DeepSeek R1 Distill Qwen 14B at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.2 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 18.7 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.2 GB at Q4_K_M quantization. On RTX 4060 Ti 8GB, it fits at Q2_K using 9.1 GB.
The recommended quantization is Q4_K_M, but on RTX 4060 Ti 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.
On RTX 4060 Ti 8GB, DeepSeek R1 Distill Qwen 14B achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10345ms using Q2_K quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on RTX 4060 Ti 8GB receives a F grade with 7.6 tok/s and 4K context.
On RTX 4060 Ti 8GB, DeepSeek R1 Distill Qwen 14B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is —, 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.
Paste this snippet into any page to show a live fit card.
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