Can I Run DeepSeek-R1-Distill-Qwen-32B on NVIDIA RTX 6000 Ada Generation?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 21 lipca 2026
Yes, comfortably — DeepSeek R1 Distill Qwen 32B at Q4_K_M needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), leaving ~25.7 GB of the NVIDIA RTX 6000 Ada Generation's 48 GB, at ~40 tok/s (est.), with room for up to 64K context.
These figures are for DeepSeek-R1-Distill-Qwen-32B, a distill of Qwen2.5-32B — 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 RTX 6000 Ada Generation Specs
| VRAM | 48 GB |
| Memory Bandwidth | 960 GB/s |
DeepSeek R1 Distill Qwen 32B on the NVIDIA RTX 6000 Ada Generation: VRAM by quantization
| Quant | VRAM needed | Fits 48 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 66.9 GB | ✗ No | — | — |
| Q8_0 | 36.9 GB | ✓ Yes | 32K | Build for this |
| Q6_K | 29.2 GB | ✓ Yes | 64K | — |
| Q5_K_M | 25.6 GB | ✓ Yes | 64K | Build for this |
| Q4_K_M | 22.3 GB | ✓ Yes | 64K | Build for this |
| Q3_K_M | 16.6 GB | ✓ Yes | 64K | — |
| Q2_K | 13.5 GB | ✓ Yes | 128K | — |
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 48 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 RTX 6000 Ada Generation: 8K 22.3 GB · 32K 28.7 GB · 128K 54.5 GB ✗. Past 128K it no longer fits 48 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 Qwen 32B on 32 GB of system RAM, CPU only: runs comfortably.
DeepSeek R1 Sizes That Fit the NVIDIA RTX 6000 Ada Generation (at 8K context)
| DeepSeek R1 Distill Qwen 32B | Q4_K_M · 22.3 GB at 8K context · ~40 tok/s (est.) |
| DeepSeek R1 Distill Qwen 14B | Q4_K_M · 10.6 GB at 8K context · ~80 tok/s (est.) |
| DeepSeek R1 Distill Llama 8B | Q4_K_M · 6.7 GB at 8K context · ~121 tok/s (est.) |
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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
Can I run DeepSeek R1 on the NVIDIA RTX 6000 Ada Generation?
Yes, comfortably — DeepSeek R1 Distill Qwen 32B at Q4_K_M needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), leaving ~25.7 GB of the NVIDIA RTX 6000 Ada Generation's 48 GB, at ~40 tok/s (est.), with room for up to 64K context.
What's the best DeepSeek R1 size for the NVIDIA RTX 6000 Ada Generation?
DeepSeek R1 Distill Qwen 32B at Q4_K_M quantization needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), estimated ~40 tokens/sec, up to 64K context.
Every DeepSeek R1 size on the NVIDIA RTX 6000 Ada Generation
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| DeepSeek R1 (671B) | 406.5 GB | Does not fit | — |
| DeepSeek R1 Distill Qwen 14B | 10.6 GB | Runs | ~80 tok/s |
| DeepSeek R1 Distill Llama 8B | 6.7 GB | Runs | ~121 tok/s |
DeepSeek R1 on Other GPUs
- DeepSeek R1 on NVIDIA L40S
- DeepSeek R1 on Apple M3 Pro
- DeepSeek R1 on NVIDIA GeForce RTX 5090
- DeepSeek R1 on Apple M4
- DeepSeek R1 on Apple M2 Pro
Popular Models on the NVIDIA RTX 6000 Ada Generation
VRAM Tier
Troubleshooting
Buying Guide
← Can I Run It? | DeepSeek R1 Model Page | NVIDIA RTX 6000 Ada Generation GPU Page | VRAM calculator | Check Your Hardware