Can DeepSeek R1 Distill 70B run on H100 NVL 188GB?

YES — Runs Great

A76Great

Estimated from fit model

DeepSeek R1 Distill 70B needs ~67.6 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~161 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) — 67.6 GB, 160.9 tok/s, Runs well

67.6 GB required188.0 GB available

36% VRAM used

Fit status

Runs well

Decode

160.9 tok/s

TTFT

1203 ms

Safe context

131K

Memory

67.6 GB / 188.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 70B on H100 NVL 188GB
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: 160.9 tok/s decode · 1.2s TTFT (warm) · 402 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	160.9 tok/s	656 ms	131K
Coding	A	Runs well	160.9 tok/s	1203 ms	131K
Agentic Coding	A	Runs well	160.9 tok/s	1750 ms	131K
Reasoning	A	Runs well	160.9 tok/s	1422 ms	131K
RAG	A	Runs well	160.9 tok/s	2188 ms	131K

Quantization options

How DeepSeek R1 Distill 70B (70B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	B65
Q3_K_S	3	34.3 GB	Low	B66
NVFP4	4	39.2 GB	Medium	B66
Q4_K_M	4	42.7 GB	Medium	B67
Q5_K_M	5	50.4 GB	High	B68
Q6_K	6	57.4 GB	High	B68
Q8_0	8	74.9 GB	Very High	A70
F16Best for your GPU	16	143.5 GB	Maximum	A74

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 H100 NVL 188GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	91.6 tok/s
Qwen 3.5 122B A10B	122B	S	254 tok/s
DeepSeek V4 Flash	284B	S	136.1 tok/s
Mistral Small 4 119B	119B	S	275.4 tok/s
GPT-OSS 120B	117B	S	96.3 tok/s

Frequently asked questions

Can H100 NVL 188GB run DeepSeek R1 Distill 70B?

Yes, H100 NVL 188GB can run DeepSeek R1 Distill 70B with a A grade (Runs well). Expected decode speed: 160.9 tok/s.

How much VRAM does DeepSeek R1 Distill 70B need?

DeepSeek R1 Distill 70B (70B parameters) requires approximately 67.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 70B?

The recommended quantization for DeepSeek R1 Distill 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 70B run at on H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek R1 Distill 70B achieves approximately 160.9 tokens per second decode speed with a time-to-first-token of 1203ms using Q4_K_M quantization.

Can H100 NVL 188GB run DeepSeek R1 Distill 70B for coding?

For coding workloads, DeepSeek R1 Distill 70B on H100 NVL 188GB receives a A grade with 160.9 tok/s and 131K context.

What context window can DeepSeek R1 Distill 70B use on H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek R1 Distill 70B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for DeepSeek R1 Distill 70B

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