DeepSeek-R1 Distill 14B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026
What GPU Do You Need for DeepSeek-R1 Distill 14B?
DeepSeek-R1 Distill 14B is the mid-size reasoning workhorse, big enough that the thinking traces genuinely sharpen answers, small enough to fit a 12GB card. We measured it on 11 GPUs (llama.cpp, Q4_K_M): 156 tok/s on the B300, ~10GB peak VRAM.
Benchmarked weights: bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF
NVIDIA B300
156.3 tok/s on DeepSeek-R1 Distill 14B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.
- 156.3 tok/s on DeepSeek-R1 Distill 14B
- 288GB, clears the DeepSeek-R1 Distill 14B floor
- Rentable by the hour rather than bought
- 1400W board rating
- Datacenter or workstation hardware, not a retail purchase
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
135.4 tok/s on DeepSeek-R1 Distill 14B, lowest launch price that still fits. Measured on our bench. 96GB of VRAM, $8,565 at launch.
- 135.4 tok/s on DeepSeek-R1 Distill 14B
- 96GB, clears the DeepSeek-R1 Distill 14B floor
- Rentable by the hour rather than bought
- 600W board rating
- Datacenter or workstation hardware, not a retail purchase
What GPU Do You Need for DeepSeek-R1 Distill 14B?, tok/s by GPU
Measured on our own bench. A card absent from this chart has not been run on this model yet, or cannot fit it.
Efficiency: tok/s per 100W drawn
Power is the average pulled during the run, sampled at 1Hz. The fastest card is often not the one here, and for anything left running this is the number that shows up on the bill.
Value: tok/s per $1,000 of MSRP
Launch price, not street price, so it ages. A speed leaderboard always crowns the most expensive card; this is the counterweight.
DeepSeek-R1 Distill 14B. Measured generation speed by GPU
| GPU | tok/s | Prompt t/s | tok/W | Avg power |
|---|---|---|---|---|
| NVIDIA B300 | 156.3 | 2964.2 | 0.48 | 328.2 W |
| NVIDIA B200 | 150.7 | 5458.1 | 0.46 | 330.8 W |
| NVIDIA H200 | 146.8 | 4674.7 | 0.72 | 203.1 W |
| NVIDIA H100 80GB HBM3 | 144.8 | 4829.4 | 0.57 | 251.9 W |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 135.4 | 7058.3 | 0.64 | 211.7 W |
| NVIDIA A100 80GB SXM4 | 87 | 2552.9 | 0.58 | 148.9 W |
| NVIDIA A100 40GB SXM4 | 86.39 | 2496.6 | 0.52 | 166.9 W |
| NVIDIA L40S | 74.6 | 5311.9 | 0.32 | 232.3 W |
| NVIDIA A10G | 46.96 | 1769.5 | 0.36 | 130.8 W |
| NVIDIA L4 | 27.39 | 1582.7 | 0.42 | 65.3 W |
| NVIDIA T4 | 19.21 | 656.2 | 0.3 | 63.1 W |
The consensus-tier member. Our thesis for local AI is that a committee of mid-size specialists beats one big generalist, and this model is a founding member of that committee. At 14B the R1 reasoning is real: it catches logic errors the 7B waves through. Run it against Qwen3 14B on the same prompt and you have a two-model panel that fits sequentially on one 12GB card; the answers agreeing is a confidence signal, and their disagreeing is a flag worth reading both traces for.
Hardware math. The ~10GB floor makes a $179 Arc B580 or an RTX 3060 12GB the cheapest hosts, though reasoning models punish slow cards twice, the L4's 27 tok/s means a thousand-token thinking trace takes over half a minute before the answer even starts. If you're running this daily, the A10G tier (51 tok/s) is the floor we'd actually recommend, and the H200's 147 tok/s at 203W is the sensible rented option.
How it compares. H100 80GB HBM3: DeepSeek-R1 Distill 14B 144.8 tok/s, Qwen2.5-Coder-14B 144.8 (15B), Uncensored 144.9, Qwen2.5-14B 144.7 (15B), Qwen2.5-Coder-14B-Instruct-abliterated 145.0. 3 of 4 beat DeepSeek-R1 Distill 14B here.
Cost on a rented GPU. 1M generated tokens of DeepSeek-R1 Distill 14B: $1.52 on a A100 40GB SXM4 ($0.47/hr, 3.2 hours), $12.33 on a B300 ($6.94/hr, 107 min, 8.1x the cost).
DeepSeek-R1 Distill 14B: cost per 1M generated tokens on rented GPUs
| GPU | Cheapest rate | Speed (tok/s) | Cost per 1M generated tokens |
|---|---|---|---|
| NVIDIA A100 40GB SXM4 | $0.47/hr | 86.39 | $1.52 |
| NVIDIA T4 | $0.14/hr | 19.21 | $1.97 |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | $1.08/hr | 135.4 | $2.21 |
| NVIDIA L40S | $0.79/hr | 74.6 | $2.94 |
| NVIDIA A100 80GB SXM4 | $0.95/hr | 87 | $3.02 |
| NVIDIA H100 80GB HBM3 | $2.14/hr | 144.8 | $4.10 |
| NVIDIA L4 | $0.44/hr | 27.39 | $4.46 |
| NVIDIA H200 | $3.59/hr | 146.8 | $6.79 |
| NVIDIA B200 | $5.98/hr | 150.7 | $11.02 |
| NVIDIA B300 | $6.94/hr | 156.3 | $12.33 |
Cheapest hourly rate we track on RunPod and Vast.ai, divided by the measured speed. Startup time and storage are extra.
Speed tiers for DeepSeek-R1 Distill 14B. 30+ tok/s: 9 (B300, B200, H200); 10-30 tok/s: 2 (L4, T4). 30 tok/s is roughly where replies outpace reading.
Reading your prompt. Before DeepSeek-R1 Distill 14B writes anything it reads the input: 7058.3 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (0.6s for a 4,000-token prompt), 656.2 on the T4 (6.1s). Long documents and big code files feel this number more than the generation speed.
VRAM for DeepSeek-R1 Distill 14B. Measured peak 8.9GB, so 12GB is the smallest common card size; smallest card it ran on: T4 (16GB). With long context: Q4_K_M 11GB (tested), Q2_K 8GB, Q3_K_M 9GB, Q5_K_M 13GB, Q6_K 15GB.
Power on DeepSeek-R1 Distill 14B. Most efficient: H200, 203W, 0.38 kWh per 1M generated tokens. Hungriest: B200, 331W, 0.61 kWh. At $0.15/kWh: $0.058 per 1M generated tokens.
Our verdict
DeepSeek-R1 Distill 14B: 156 tok/s peak, ~10GB floor: the smallest distill whose reasoning we'd call trustworthy, and a natural panel-mate for Qwen3 14B in a consensus setup on a 12GB card. Remember the trace tax: slow cards make reasoning models feel twice as slow.
FAQ
- Can a 12GB card run DeepSeek-R1 Distill 14B?
- Yes, ~10GB measured peak at Q4_K_M fits 12GB cards like the Arc B580 ($179) or RTX 3060 12GB, with modest context headroom. For long traces and contexts, 16GB is more comfortable.
- Is the 14B distill good enough, or do I need the 32B?
- 14B is where the reasoning becomes dependable for everyday problems. The 32B is meaningfully stronger on hard math and code. But it needs 24GB and generates at roughly half the speed. Many setups run 14B daily and reserve 32B-class panels for the hard 10%.
- How should I use it in a consensus setup?
- Pair it with Qwen3 14B, same size, different lineage. One 12GB card runs both sequentially on the same prompt. Agreement is a confidence signal; disagreement tells you exactly which answers to scrutinize.
- What speed should I expect?
- 156 tok/s on the B300 down to 27 on an L4. Because it thinks before answering, we'd treat ~50 tok/s (A10G tier) as the practical minimum for interactive use.
- DeepSeek 14B or Phi-4 14B?
- Phi-4 measured faster (177 vs 156 tok/s peak) but answers directly; this distill spends tokens reasoning. For math and debugging the trace usually wins; for quick factual work Phi's directness is the better economy.