Can you run Deepseek R1 Distill Qwen 14B locally?
Deepseek family · · 14.8B parameters · Hugging Face model card
Deepseek R1 Distill Qwen 14B is a 14.8B-parameter model open-weight model with a 131,072-token context window, usable locally with roughly 16 GB of memory at Q4-class quantization (calculated; measured speeds vary by hardware).
Minimum: 16 GB+ memory (Q4_K_M, calculated) · Recommended: 16 GB+ memory for full context (Q4_K_M, calculated)
How much VRAM does Deepseek R1 Distill Qwen 14B need at each quantization?
Deepseek R1 Distill Qwen 14B needs 9.8 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 8.9 GB | 9.8 GB | 9.8 GB |
| Q5_K_M | 5.7 | 10.5 GB | 11.6 GB | 11.6 GB |
| Q6_K | 6.6 | 12.2 GB | 13.4 GB | 13.4 GB |
| Q8_0 | 8.5 | 15.7 GB | 17.3 GB | 17.3 GB |
| FP16 | 16 | 29.5 GB | 32.5 GB | 32.5 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (48 layers, 8 KV heads, head dim). Source: HF-verified 2026-09-12 via api (params=14.77B ctx=131072 license=mit); VRAM figures are documented calculations, not measurements
Which GPUs can run Deepseek R1 Distill Qwen 14B locally?
At Q4_K_M with a 4k context, Deepseek R1 Distill Qwen 14B needs 9.8 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs Deepseek R1 Distill Qwen 14B comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc A770 16GB | 16 GB | 560 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 Ti SUPEREOL | 16 GB | 672 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080EOL | 16 GB | 716 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080 SUPER | 16 GB | 736 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 Ti 16GB | 16 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 Ti | 16 GB | 896 GB/s | Consumer GPU | Check price |
| GeForce RTX 5080 | 16 GB | 960 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 2000 Blackwell | 16 GB | 288 GB/s | Pro GPU | Check price |
| Radeon RX 6800 XTEOL | 16 GB | 512 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 XT | 16 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 7800 XT | 16 GB | 624 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 XT | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 7900 XT | 20 GB | 800 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090EOL | 24 GB | 936 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090 TiEOL | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| NVIDIA RTX PRO 4000 Blackwell | 24 GB | 672 GB/s | Pro GPU | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | Consumer GPU | Check price |
| AMD Radeon Pro W7800 | 32 GB | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB | 1792 GB/s | Consumer GPU | Check price |
| NVIDIA RTX 5000 Ada | 32 GB | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB | 1555 GB/s | Data Center GPU | |
| AMD Radeon Pro W7900 | 48 GB | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB | 768 GB/s | Pro GPU | Check price |
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Minimum GPUs that fit Deepseek R1 Distill Qwen 14B at Q4_K_M
These GPUs hold the model but leave little headroom — keep contexts short.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc B570 | 10 GB | 380 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 10GBEOL | 10 GB | 760 GB/s | Consumer GPU | Check price |
| GeForce RTX 2080 TiEOL | 11 GB | 616 GB/s | Consumer GPU | Check price |
| Arc B580 | 12 GB | 456 GB/s | Consumer GPU | Check price |
| GeForce RTX 3060 12GB | 12 GB | 360 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 TiEOL | 12 GB | 912 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070EOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 SUPEREOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 | 12 GB | 672 GB/s | Consumer GPU | Check price |
| Radeon RX 7700 XT | 12 GB | 432 GB/s | Consumer GPU | Check price |
Needs 2+ GPUs to run Deepseek R1 Distill Qwen 14B
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 9.8 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Arc A580 | 8 GB ×2 | 512 GB/s | Consumer GPU | Check price |
| Arc A750 | 8 GB ×2 | 512 GB/s | Consumer GPU | Check price |
| GeForce RTX 3070EOL | 8 GB ×2 | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 | 8 GB ×2 | 272 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 8GB | 8 GB ×2 | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 5050 | 8 GB ×2 | 320 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 | 8 GB ×2 | 448 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 | 8 GB ×2 | 288 GB/s | Consumer GPU | Check price |
Can a Mac run Deepseek R1 Distill Qwen 14B?
Yes — these Apple Silicon machines fit Deepseek R1 Distill Qwen 14B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M3 | 24 GB | 18 GB | 150 GB/s |
| Apple M4 | 32 GB | 24 GB | 120 GB/s |
| Apple M3 Pro | 36 GB | 27 GB | 300 GB/s |
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| M3 Ultra (Mac Studio, 96 GB) | 96 GB | 72 GB | 819 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M3 Ultra (Mac Studio, 256 GB) | 256 GB | 192 GB | 819 GB/s |
Can Deepseek R1 Distill Qwen 14B run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Deepseek R1 Distill Qwen 14B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson Orin NX 16GB Super | 16 GB | 102 GB/s | Edge Compute Device |
| Intel Core Ultra 9 288V (Lunar Lake) | 32 GB | 136 GB/s | NPU Chip |
| Jetson AGX Orin 32GB | 32 GB | 204 GB/s | Edge Compute Device |
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run Deepseek R1 Distill Qwen 14B?
These mini PCs and workstations from our database fit Deepseek R1 Distill Qwen 14B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.