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.

DeepSeek-R1 Distill 14B · 10 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 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 10 GPUs (llama.cpp, Q4_K_M): 156 tok/s on the B300, ~10GB peak VRAM.

Benchmarked weights: bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF

156.33tok/s

Fastest: NVIDIA B300

measured, 3-run llama-bench

~10GB

VRAM needed (measured peak)

GPU-independent, applies to every card

GPUs measured

same pinned harness

0.72tok/W

Most efficient: NVIDIA H200

real power sampling, not TDP

What GPU Do You Need for DeepSeek-R1 Distill 14B?, tok/s, fastest 10

NVIDIA B300

156.33 tok/s

NVIDIA B200

150.69 tok/s

NVIDIA H200

146.82 tok/s

NVIDIA H100 80GB HBM3

144.82 tok/s

NVIDIA RTX PRO 6000 Blackwell Workstation Edition

135.41 tok/s

NVIDIA A100 80GB SXM4

87 tok/s

NVIDIA A100 40GB SXM4

86.76 tok/s

NVIDIA L40S

74.34 tok/s

NVIDIA A10G

50.79 tok/s

NVIDIA L4

27.23 tok/s

Measured on our own bench. A card absent from this chart has not been run on this model yet, or cannot fit it.

DeepSeek-R1 Distill 14B. Measured generation speed by GPU

NVIDIA B300156.33

NVIDIA B200150.69

NVIDIA H200146.82

NVIDIA H100 80GB HBM3144.82

NVIDIA RTX PRO 6000 Blackwell Workstation Edition135.41

NVIDIA A100 80GB SXM487

NVIDIA A100 40GB SXM486.76

NVIDIA L40S74.34

NVIDIA A10G50.79

NVIDIA L427.23

GPU	tok/s	Prompt t/s	tok/W	Avg power
NVIDIA B300	156.33	2964.2	0.48	328.2 W
NVIDIA B200	150.69	5458.1	0.46	330.8 W
NVIDIA H200	146.82	4674.7	0.72	203.1 W
NVIDIA H100 80GB HBM3	144.82	4829.4	0.57	251.9 W
NVIDIA RTX PRO 6000 Blackwell Workstation Edition	135.41	7058.3	0.64	211.7 W
NVIDIA A100 80GB SXM4	87	2552.9	0.58	148.9 W
NVIDIA A100 40GB SXM4	86.76	2548.8	0.59	146.3 W
NVIDIA L40S	74.34	5224.4	0.44	169.1 W
NVIDIA A10G	50.79	2148	0.41	122.9 W
NVIDIA L4	27.23	1527.4	0.48	56.5 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.

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.