Can NVIDIA GeForce RTX 4070 Ti SUPER run DeepSeek-R1-Distill-Qwen-14B?

This page was written and translated automatically by AI and checked by software against its sources, with no human editing. Specifications come from the sources listed; memory requirements and speeds are estimates calculated with published formulas. How we use AI

Fits By llama.cpp’s default load check, DeepSeek-R1-Distill-Qwen-14B fits on NVIDIA GeForce RTX 4070 Ti SUPER at Q5_K_M with a context of 8,192 tokens. One card needs 12.7 GiB available for “Fits”.

At Q5_K_M, the longest context that fits is 9,216 tokens.

“Fits” means llama.cpp's default --fit load check would place all layers on the GPU: our estimate of llama.cpp's buffers, plus its error margin, plus llama.cpp's 1 GiB per-card reserve, is within the memory counted as available. It does not promise that peak memory while generating fits. How results are decided

Values updated Oct 19, 2026 · Calculator

Run profile: llama.cpp, CUDA back end (NVIDIA discrete GPU), all layers on the GPU, Flash Attention on, micro-batch (-ub) 512, F16 KV cache, context (-c) as shown; “Fits” is judged with llama-server’s default 4 parallel sequences (-np auto, unified KV cache) and “Doesn’t fit” with 1 sequence; idle GPU with no other programs using its memory.

Memory counted as available for “Fits” (C_low): 12.88 GiB per card = 16 GiB nominal − 1,054 MiB (the largest gap between nominal and reported total measured on consumer and workstation GPUs in this profile, used because no public log reports this GPU’s own total) − 2,112 MiB (the largest start-up and runtime use seen in this profile’s public logs), rounded down to a multiple of 64 MiB.

These results assume the card’s default configuration. Settings that reduce usable memory (for example ECC, where the card supports it) are not covered.

Memory used for “Doesn’t fit” (C_high): the full 16 GiB per card. Run profiles and their measurements

Memory needed by quantization and context

Available memory needed for “Fits” (X, GiB per card) and the result on NVIDIA GeForce RTX 4070 Ti SUPER.
Quantization	4,096	8,192	32,768	131,072
F16	29.5 GiB Doesn't fit	30.3 GiB Doesn't fit	35.1 GiB Doesn't fit	54.3 GiB Doesn't fit
Q8_0	16.7 GiB Tight	17.5 GiB Tight	22.3 GiB Doesn't fit	41.4 GiB Doesn't fit
Q6_K	13.4 GiB Tight	14.2 GiB Tight	18.9 GiB Tight	38.1 GiB Doesn't fit
Q5_K_M	11.9 GiB Fits	12.7 GiB Fits	17.5 GiB Tight	36.6 GiB Doesn't fit
Q4_K_M	10.5 GiB Fits	11.3 GiB Fits	16.1 GiB Tight	35.2 GiB Doesn't fit
Q3_K_M	9.0 GiB Fits	9.8 GiB Fits	14.5 GiB Tight	33.7 GiB Doesn't fit
Q2_K	7.5 GiB Fits	8.3 GiB Fits	13.1 GiB Tight	32.2 GiB Doesn't fit

How fast can it generate?

At Q5_K_M, the theoretical ceiling is about 58.0 tokens per second (672 GB/s ÷ 11.58 GB read per token).

The speed ceiling is memory bandwidth divided by the bytes read per generated token. It is a theoretical upper bound; public llama.cpp benchmarks typically reach about half to four-fifths of it, and small models and mixture-of-experts models often less. Public benchmarks

Alternatives

Other 16 GB GPUs where DeepSeek-R1-Distill-Qwen-14B fits

Sources

Model page, with every quantization and its files: DeepSeek-R1-Distill-Qwen-14B
GPU page, with each specification and its sources: NVIDIA GeForce RTX 4070 Ti SUPER
Model files on Hugging Face (pinned revisions): deepseek-ai/DeepSeek-R1-Distill-Qwen-14B@1df8507, unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF@7b05b58
Memory size of NVIDIA GeForce RTX 4070 Ti SUPER from: Wikipedia: GeForce RTX 40 series (CC BY-SA 4.0), GetDeploying GPU Models (CC BY 4.0)
Methodology