Can NVIDIA GeForce RTX 5070 run DeepSeek-R1-Distill-Llama-8B?

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Fits By llama.cpp’s default load check, DeepSeek-R1-Distill-Llama-8B fits on NVIDIA GeForce RTX 5070 at Q6_K with a context of 8,192 tokens. One card needs 8.4 GiB available for “Fits”.

At Q6_K, the longest context that fits is 12,032 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): 8.88 GiB per card = 12 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 12 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 5070.
Quantization	4,096	8,192	32,768	131,072
BF16	16.5 GiB Doesn't fit	17.0 GiB Doesn't fit	20.3 GiB Doesn't fit	33.1 GiB Doesn't fit
Q8_0	9.6 GiB Tight	10.2 GiB Tight	13.4 GiB Tight	26.2 GiB Doesn't fit
Q6_K	7.8 GiB Fits	8.4 GiB Fits	11.6 GiB Tight	24.4 GiB Doesn't fit
Q5_K_M	7.1 GiB Fits	7.6 GiB Fits	10.8 GiB Tight	23.6 GiB Doesn't fit
Q4_K_M	6.3 GiB Fits	6.9 GiB Fits	10.1 GiB Tight	22.9 GiB Doesn't fit
Q4_0	6.1 GiB Fits	6.6 GiB Fits	9.8 GiB Tight	22.7 GiB Doesn't fit
IQ4_XS	5.9 GiB Fits	6.4 GiB Fits	9.6 GiB Tight	22.4 GiB Doesn't fit
Q3_K_M	5.5 GiB Fits	6.0 GiB Fits	9.3 GiB Tight	22.1 GiB Doesn't fit
Q2_K	4.7 GiB Fits	5.3 GiB Fits	8.5 GiB Fits	21.3 GiB Doesn't fit

How fast can it generate?

At Q6_K, the theoretical ceiling is about 92.9 tokens per second (672 GB/s ÷ 7.23 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 12 GB GPUs where DeepSeek-R1-Distill-Llama-8B fits

Sources

Model page, with every quantization and its files: DeepSeek-R1-Distill-Llama-8B
GPU page, with each specification and its sources: NVIDIA GeForce RTX 5070
Model files on Hugging Face (pinned revisions): deepseek-ai/DeepSeek-R1-Distill-Llama-8B@6a6f4aa, unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF@615f893
Memory size of NVIDIA GeForce RTX 5070 from: Wikipedia: List of Nvidia graphics processing units (CC BY-SA 4.0), GetDeploying GPU Models (CC BY 4.0)
Methodology