Can the NVIDIA RTX PRO 6000 Blackwell run DeepSeek R1-Distill 32B?
DeepSeek R1-Distill-Qwen-32B runs locally on a 24 GB GPU at Q4_K_M, which needs about 22.7 GB of VRAM with a 4k-token context. A 32 GB GPU runs the model comfortably, and reasoning chains of 8k tokens or more raise the KV cache, so 24 GB is the floor rather than the sweet spot.
At Q4_K_M quantization with 4,096 context, the DeepSeek R1-Distill 32B requires approximately 22.72GB of VRAM. The NVIDIA RTX PRO 6000 Blackwell has 96GB available (73.28GB headroom).
VRAM Breakdown
| Component | Size (GB) | Source |
|---|---|---|
| Model weights (Q4_K_M) | 19.68 | calculated |
| Loading overhead (10%) | 1.97 | calculated |
| KV cache @ 4,096 context | 1.07 | calculated |
| Total required | 22.72 | |
| GPU memory available | 96 | vendor_spec |
| Headroom / shortfall | +73.28 |
Quantization Options
| Quant | Weights (GB) | Total @ 4K (GB) | Total @ 32K (GB) | Fits NVIDIA RTX PRO 6000 Blackwell? |
|---|---|---|---|---|
| Q4_K_M | 19.68 | 22.72 | 30.24 | Yes |
| Q5_K_M | 23.37 | 26.78 | 34.3 | Yes |
| Q6_K | 27.06 | 30.84 | 38.36 | Yes |
| Q8_0 | 34.85 | 39.41 | 46.93 | Yes |
| FP16 | 65.6 | 73.23 | 80.75 | Yes |
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Frequently Asked Questions
Can the DeepSeek R1-Distill 32B run on 96GB VRAM?
Yes. At Q4_K_M quantization with 4K context, the DeepSeek R1-Distill 32B needs approximately 22.72GB. Your 96GB of usable memory has 73.28GB headroom.
What is the maximum context length I can use on the NVIDIA RTX PRO 6000 Blackwell?
The DeepSeek R1-Distill 32B supports up to 131,072 tokens of context. Larger context increases KV-cache memory requirements. At 4K context the model needs ~22.72GB; at 32K it needs more.
Which quantization should I use with the NVIDIA RTX PRO 6000 Blackwell?
Q4_K_M is the recommended starting point — it offers the best speed-to-quality tradeoff. If you have VRAM headroom, Q6_K or Q8_0 improves quality at the cost of speed. FP16 is only for inference servers with ample memory.
Can an RTX 3090 run DeepSeek R1-Distill 32B?
Yes. DeepSeek R1-Distill-Qwen-32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k context, which fits the RTX 3090’s 24 GB. Long reasoning traces can exceed the cache budget, so keep context below roughly 16k tokens on 24 GB cards.
How much VRAM does DeepSeek R1-Distill 32B need?
DeepSeek R1-Distill-Qwen-32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k context and about 30.2 GB with a 32k context, calculated from 19.68 GB of weights plus overhead plus the 64-layer KV cache.
Related Pages
Full DeepSeek R1-Distill 32B fit page · All AI models · NVIDIA RTX PRO 6000 Blackwell specs · VRAM calculator
VRAM figures are calculated from model architecture parameters using established formulas. Benchmark data is measured and cited per row. GPU memory is vendor_spec from manufacturer datasheets.