Can DeepSeek R1 Distill 70B run on NVIDIA H100 80GB?

YES — Runs Great

A82Great

Estimated from fit model

DeepSeek R1 Distill 70B needs ~56.5 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~72 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 56.5 GB, 71.7 tok/s, Runs well

56.5 GB required80.0 GB available

71% VRAM used

Fit status

Runs well

Decode

71.7 tok/s

TTFT

2701 ms

Safe context

93K

Memory

56.5 GB / 80.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 70B on NVIDIA H100 80GB
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: 71.7 tok/s decode · 2.7s TTFT (warm) · 179 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	71.7 tok/s	1473 ms	93K
Coding	A	Runs well	71.7 tok/s	2701 ms	93K
Agentic Coding	A	Runs well	71.7 tok/s	3929 ms	93K
Reasoning	A	Runs well	71.7 tok/s	3192 ms	93K
RAG	A	Runs well	71.7 tok/s	4912 ms	93K

Inference speed

DeepSeek R1 Distill 70B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 70B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~18 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	18.0	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	15.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	14.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.6	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.2	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	9.5	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	8.7	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.7	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	5.7	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.4	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.3	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 Distill 70B (70B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	B70
Q3_K_S	3	34.3 GB	Low	A72
NVFP4	4	39.2 GB	Medium	A73
Q4_K_M	4	42.7 GB	Medium	A74
Q5_K_M	5	50.4 GB	High	A74
Q6_KBest for your GPU	6	57.4 GB	High	A74
Q8_0	8	74.9 GB	Very High	F0
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 NVIDIA H100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	29 tok/s
Qwen 3.5 122B A10B	122B	S	86 tok/s
Mistral Small 4 119B	119B	A	91.3 tok/s
GPT-OSS 120B	117B	A	33 tok/s
Command A 111B	111B	S	38.3 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run DeepSeek R1 Distill 70B?

Yes, NVIDIA H100 80GB can run DeepSeek R1 Distill 70B with a A grade (Runs well). Expected decode speed: 71.7 tok/s.

How much VRAM does DeepSeek R1 Distill 70B need?

DeepSeek R1 Distill 70B (70B parameters) requires approximately 56.5 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 NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek R1 Distill 70B achieves approximately 71.7 tokens per second decode speed with a time-to-first-token of 2701ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run DeepSeek R1 Distill 70B for coding?

For coding workloads, DeepSeek R1 Distill 70B on NVIDIA H100 80GB receives a A grade with 71.7 tok/s and 93K context.

What context window can DeepSeek R1 Distill 70B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek R1 Distill 70B can safely use up to 93K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for DeepSeek R1 Distill 70B

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