16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can DeepSeek R1 Distill Qwen 14B run on RTX 4070 Super 12GB?
YES — With Offload
C51Usable
○Estimated from fit model
DeepSeek R1 Distill Qwen 14B needs ~12.3 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 12.3 GB, 34.1 tok/s, Runs with offload (needs ~0.2 GB host RAM)
12.3 GB required12.0 GB available
103% VRAM needed
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
34.1 tok/s
TTFT
5680 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill Qwen 14B on RTX 4070 Super 12GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 34.1 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 47.7 tok/s | 2213 ms | 13K |
| Coding | C | Runs with offload | 32.5 tok/s | 5964 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s | 10757 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 34.1 tok/s | 6712 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 26.2 tok/s | 13446 ms |
Quantization options
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C53 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4 | 4 |
Get started
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 startUpgrade options
Hardware that runs DeepSeek R1 Distill Qwen 14B well
16 GB VRAM (+4)
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Adds memory headroom for longer context windows and future model growth.25.8 tok/s decode
Adds memory headroom for longer context windows and future model growth.
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16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP