Can DeepSeek R1 Distill 70B run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

A78Great

○Estimated from fit model

DeepSeek R1 Distill 70B needs ~58.4 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 58.4 GB, 34.2 tok/s, Runs well

58.4 GB required96.0 GB available

61% VRAM used

Fit status

Runs well

Decode

34.2 tok/s

TTFT

5667 ms

Safe context

131K

Memory

58.4 GB / 96.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 70B on RTX PRO 6000 Blackwell Server Edition 96GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 34.2 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	34.2 tok/s	3091 ms	131K
Coding	A	Runs well	34.2 tok/s	5667 ms	131K
Agentic Coding	A	Runs well	34.2 tok/s	8242 ms	131K
Reasoning	A	Runs well	34.2 tok/s	6697 ms	131K
RAG	A	Runs well	34.2 tok/s	10303 ms	131K

Quantization options

How DeepSeek R1 Distill 70B (70B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	B69
Q3_K_S	3	34.3 GB	Low	A70
NVFP4	4	39.2 GB	Medium	A71
Q4_K_M	4	42.7 GB	Medium	A72
Q5_K_M	5	50.4 GB	High	A74
Q6_K	6	57.4 GB	High	A74
Q8_0Best for your GPU	8	74.9 GB	Very High	A74
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 Distill 70B on your machine.

Run

ollama run deepseek-r1:70b

Your hardware

More models your RTX PRO 6000 Blackwell Server Edition 96GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	19.4 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	53.9 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	58.5 tok/s
👁 OpenAI GPT-OSS 120B	117B	S	20.4 tok/s
👁 Cohere Command A 111B	111B	S	21.6 tok/s

Frequently asked questions

See all results for RTX PRO 6000 Blackwell Server Edition 96GB See all hardware for DeepSeek R1 Distill 70B

URL: https://willitrunai.com/can-run/deepseek-r1-70b-on-rtx-pro-6000-blackwell-server-96gb

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