DeepSeek R1 Distill 32B needs ~26.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
2.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2 GB host RAM)
Decode
19.2 tok/s
TTFT
10096 ms
Safe context
5K
Memory
26.7 GB / 24.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~0.6 GB host RAM) | 22.4 tok/s | 4717 ms | 5K |
| Coding | B | Very compromised (needs ~2 GB host RAM) | 19.2 tok/s | 10096 ms | 5K |
| Agentic Coding | F | Too heavy | 14.5 tok/s | 19397 ms | 5K |
| Reasoning | B | Very compromised (needs ~2 GB host RAM) | 19.2 tok/s | 11931 ms | 5K |
| RAG | F | Too heavy | 14.5 tok/s | 24246 ms | 5K |
How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | A76 |
Q3_K_S | 3 | 15.7 GB | Low | A75 |
NVFP4Best for your GPU | 4 | 17.9 GB | Medium | A75 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.
Run
ollama run deepseek-r1:32bUpgrade-Optionen
RTX 5090 32GBGünstige WahlRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 124%.
ca. $1,999 MSRP
RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-VerhältnisRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 117%.
ca. $2,499 MSRP
RTX 5000 Ada 32GBNVIDIA-UpgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 33%.
ca. $4,000 MSRP
Yes, RTX PRO 4000 Blackwell 24GB can run DeepSeek R1 Distill 32B with a B grade (Very compromised (needs ~2 GB host RAM)). Expected decode speed: 19.2 tok/s.
DeepSeek R1 Distill 32B (32B parameters) requires approximately 26.7 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 4000 Blackwell 24GB, DeepSeek R1 Distill 32B achieves approximately 19.2 tokens per second decode speed with a time-to-first-token of 10096ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 32B on RTX PRO 4000 Blackwell 24GB receives a B grade with 19.2 tok/s and 5K context.
On RTX PRO 4000 Blackwell 24GB, DeepSeek R1 Distill 32B can safely use up to 5K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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