DeepSeek R1 Distill 14B needs ~13.6 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~26 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
25.9 tok/s
TTFT
7473 ms
Safe context
7K
Memory
13.6 GB / 12.0 GB
Offload
10%
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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.1 GB host RAM) | 32.9 tok/s | 3205 ms | 7K |
| Coding | B | Very compromised (needs ~1 GB host RAM) | 25.9 tok/s | 7473 ms | 7K |
| Agentic Coding | F | Too heavy | 17.2 tok/s | 16403 ms | 7K |
| Reasoning | B | Very compromised (needs ~1 GB host RAM) | 25.9 tok/s | 8832 ms | 7K |
| RAG | F | Too heavy | 15.1 tok/s | 23252 ms | 7K |
How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A77 |
Q3_K_S | 3 | 6.9 GB | Low | A76 |
NVFP4 | 4 | 7.8 GB | Medium | A76 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | A76 |
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 14B on your machine.
Run
ollama run deepseek-r1Opciones de mejora
RTX 5060 Ti 16GBOpción económicaElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 32%.
~$449 MSRP
RTX 4060 Ti 16GBMejor relación calidad-precioElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$499 MSRP
RTX 5070 Ti 16GBMejora NVIDIAElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 171%.
~$749 MSRP
Yes, RTX 4080 Laptop 12GB can run DeepSeek R1 Distill 14B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 25.9 tok/s.
DeepSeek R1 Distill 14B (14B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Laptop 12GB, DeepSeek R1 Distill 14B achieves approximately 25.9 tokens per second decode speed with a time-to-first-token of 7473ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 14B on RTX 4080 Laptop 12GB receives a B grade with 25.9 tok/s and 7K context.
On RTX 4080 Laptop 12GB, DeepSeek R1 Distill 14B can safely use up to 7K tokens of context. 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-14b-on-rtx-4080-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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