RTX 5060 Ti 16GBBudget pick16 GB VRAM (+4)448 GB/s (+112)
C
Raises estimated decode speed by about 47%.31.5 tok/s decode
Raises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
DeepSeek R1 Distill Qwen 14B needs ~12.3 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~22 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
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
21.5 tok/s
TTFT
8986 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 30.2 tok/s | 3502 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 21.5 tok/s | 8986 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 16.5 tok/s | 17019 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 21.5 tok/s | 10620 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 16.5 tok/s | 21274 ms | 13K |
Inference speed
Estimated decode speed (tokens/sec) for DeepSeek R1 Distill Qwen 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 140.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 89.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 80.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 76.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 75.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 65.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 19.5 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 7.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on RTX 3500 Ada Laptop 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 | 7.8 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C52 |
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 startUpgrade options
RTX 5060 Ti 16GBBudget pickRaises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 4060 Ti 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA upgradeRaises estimated decode speed by about 203%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Yes, RTX 3500 Ada Laptop 12GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 21.5 tok/s.
DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill Qwen 14B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3500 Ada Laptop 12GB, DeepSeek R1 Distill Qwen 14B achieves approximately 21.5 tokens per second decode speed with a time-to-first-token of 8986ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill Qwen 14B on RTX 3500 Ada Laptop 12GB receives a C grade with 21.5 tok/s and 13K context.
On RTX 3500 Ada Laptop 12GB, DeepSeek R1 Distill Qwen 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--deepseek-r1-distill-qwen-14b-gguf-on-rtx-3500-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: