Can I Run DeepSeek-R1-Distill-Llama-8B on NVIDIA GeForce RTX 3080 (10GB)?
作者： Jakub Rusinowski · 最后更新： 2026年7月12日
Yes, comfortably — DeepSeek R1 Distill Llama 8B at Q4_K_M needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), leaving ~3.3 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~74 tok/s (est.), with room for up to 32K context.
These figures are for DeepSeek-R1-Distill-Llama-8B, a distill of Llama-3.1-8B — not the full DeepSeek R1. The full DeepSeek R1 (671B) needs about 405 GB of weights at Q4_K_M and is a different model.
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NVIDIA GeForce RTX 3080 (10GB) Specs
| VRAM | 10 GB |
| Memory Bandwidth | 760 GB/s |
DeepSeek R1 Distill Llama 8B on the NVIDIA GeForce RTX 3080 (10GB): VRAM by quantization
| Quant | VRAM needed | Fits 10 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 17.9 GB | ✗ No | — | — |
| Q8_0 | 10.4 GB | ✗ No | — | — |
| Q6_K | 8.4 GB | ✓ Yes | 16K | — |
| Q5_K_M | 7.5 GB | ✓ Yes | 16K | — |
| Q4_K_M | 6.7 GB | ✓ Yes | 32K | — |
| Q3_K_M | 5.3 GB | ✓ Yes | 32K | — |
| Q2_K | 4.5 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 10 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 3080 (10GB): 8K 6.7 GB · 32K 9.9 GB · 128K 22.8 GB ✗. Past 128K it no longer fits 10 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? DeepSeek R1 Distill Llama 8B on 8 GB of system RAM, CPU only: runs.
DeepSeek R1 Sizes That Fit the NVIDIA GeForce RTX 3080 (10GB) (at 8K context)
| DeepSeek R1 Distill Llama 8B | Q4_K_M · 6.7 GB at 8K context · ~74 tok/s (est.) |
At 2 hrs/day, buying (~$699) beats renting at $0.34/hr after about 2.9 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
Does the NVIDIA GeForce RTX 3080 (10GB) have enough VRAM for DeepSeek R1?
Yes, comfortably — DeepSeek R1 Distill Llama 8B at Q4_K_M needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), leaving ~3.3 GB of the NVIDIA GeForce RTX 3080 (10GB)'s 10 GB, at ~74 tok/s (est.), with room for up to 32K context.
Which quantization of DeepSeek R1 should I use on the NVIDIA GeForce RTX 3080 (10GB)?
DeepSeek R1 Distill Llama 8B at Q4_K_M quantization needs 6.7 GB at 8K context (4.8 GB weights + 1.9 GB KV cache/overhead), estimated ~74 tokens/sec, up to 32K context.
Every DeepSeek R1 size on the NVIDIA GeForce RTX 3080 (10GB)
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| DeepSeek R1 (671B) | 406.5 GB | Does not fit | — |
| DeepSeek R1 Distill Qwen 32B | 22.3 GB | Does not fit | — |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | Runs at Q3_K_M | — |
DeepSeek R1 on Other GPUs
- DeepSeek R1 on Intel Arc B570
- DeepSeek R1 on NVIDIA GeForce RTX 5070
- DeepSeek R1 on NVIDIA GeForce RTX 5060 Ti 8GB
- DeepSeek R1 on NVIDIA GeForce RTX 5060
- DeepSeek R1 on NVIDIA GeForce RTX 4070 Ti
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