Can NVIDIA GeForce RTX 3080 10GB run DeepSeek-R1-Distill-Qwen-7B?

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Fits By llama.cpp’s default load check, DeepSeek-R1-Distill-Qwen-7B fits on NVIDIA GeForce RTX 3080 10GB at Q5_K_M with a context of 8,192 tokens. One card needs 6.7 GiB available for “Fits”.

At Q5_K_M, the longest context that fits is 10,240 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): 6.88 GiB per card = 10 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 10 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 3080 10GB.
Quantization	4,096	8,192	32,768	131,072
F16	15.4 GiB Doesn't fit	15.6 GiB Doesn't fit	17.1 GiB Doesn't fit	22.8 GiB Doesn't fit
Q8_0	8.9 GiB Tight	9.1 GiB Tight	10.6 GiB Tight	16.3 GiB Doesn't fit
Q6_K	7.2 GiB Tight	7.5 GiB Tight	8.9 GiB Tight	14.6 GiB Doesn't fit
Q5_K_M	6.5 GiB Fits	6.7 GiB Fits	8.2 GiB Tight	13.9 GiB Doesn't fit
Q4_K_M	5.8 GiB Fits	6.1 GiB Fits	7.5 GiB Tight	13.2 GiB Doesn't fit
Q3_K_M	5.0 GiB Fits	5.3 GiB Fits	6.7 GiB Fits	12.4 GiB Tight
Q2_K	4.3 GiB Fits	4.6 GiB Fits	6.0 GiB Fits	11.7 GiB Tight

How fast can it generate?

At Q5_K_M, the theoretical ceiling is about 137 tokens per second (760 GB/s ÷ 5.53 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 10 GB GPUs where DeepSeek-R1-Distill-Qwen-7B fits

No other GPU with the same memory size fits this model.

Sources

Model page, with every quantization and its files: DeepSeek-R1-Distill-Qwen-7B
GPU page, with each specification and its sources: NVIDIA GeForce RTX 3080 10GB
Model files on Hugging Face (pinned revisions): deepseek-ai/DeepSeek-R1-Distill-Qwen-7B@916b56a, unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF@097680e
Memory size of NVIDIA GeForce RTX 3080 10GB from: Wikipedia: GeForce RTX 30 series (CC BY-SA 4.0), GetDeploying GPU Models (CC BY 4.0)
Methodology