Can DeepSeek V3.2 run on NVIDIA H100 80GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V3.2 needs ~419.0 GB but NVIDIA H100 80GB only has 80.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: HighStack: BasicBottleneck: Memory capacity

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) — 419.0 GB, exceeds 80.0 GB available

419.0 GB required80.0 GB available

524% VRAM needed

339.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.3 tok/s

TTFT

58771 ms

Safe context

Memory

419.0 GB / 80.0 GB

Offload

80%

Memory breakdown

Weights409.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V3.2 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: 3.3 tok/s decode · 58.8s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 419.0 GB, but this setup only exposes 80.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.3 tok/s	32057 ms	4K
Coding	F	Too heavy	3.3 tok/s	58771 ms	4K
Agentic Coding	F	Too heavy	3.3 tok/s	85484 ms	4K
Reasoning	F	Too heavy	3.3 tok/s	69456 ms	4K
RAG	F	Too heavy	3.3 tok/s	106856 ms	4K

Quantization options

How DeepSeek V3.2 (671B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	261.7 GB	Low	F0
Q3_K_S	3	328.8 GB	Low	F0
NVFP4	4	375.8 GB	Medium	F0
Q4_K_M	4	409.3 GB	Medium	F0
Q5_K_M	5	483.1 GB	High	F0
Q6_K	6	550.2 GB	High	F0
Q8_0	8	718.0 GB	Very High	F0
F16	16	1375.6 GB	Maximum	F0

Frequently asked questions

Can NVIDIA H100 80GB run DeepSeek V3.2?

No, DeepSeek V3.2 requires more memory than NVIDIA H100 80GB provides.

How much VRAM does DeepSeek V3.2 need?

DeepSeek V3.2 (671B parameters) requires approximately 419.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek V3.2?

The recommended quantization for DeepSeek V3.2 is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek V3.2 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek V3.2 achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58771ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run DeepSeek V3.2 for coding?

For coding workloads, DeepSeek V3.2 on NVIDIA H100 80GB receives a F grade with 3.3 tok/s and 4K context.

What context window can DeepSeek V3.2 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek V3.2 can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V3.2 feels slow on NVIDIA H100 80GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for NVIDIA H100 80GB See all hardware for DeepSeek V3.2

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<iframe src="https://willitrunai.com/embed/deepseek-v3.2-671b-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

3.3 tok/s

32057 ms

Coding

Too heavy

3.3 tok/s

58771 ms

Agentic Coding

Too heavy

3.3 tok/s

85484 ms

Reasoning

Too heavy

3.3 tok/s

69456 ms

RAG

Too heavy

3.3 tok/s

106856 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

261.7 GB

Low

Q3_K_S

328.8 GB

Low

NVFP4

375.8 GB

Medium

Q4_K_M

409.3 GB

Medium

Q5_K_M

483.1 GB

High

Q6_K

550.2 GB

High

Q8_0

718.0 GB

Very High

F16

1375.6 GB

Maximum

Frequently asked questions

Can NVIDIA H100 80GB run DeepSeek V3.2?

No, DeepSeek V3.2 requires more memory than NVIDIA H100 80GB provides.

How much VRAM does DeepSeek V3.2 need?

DeepSeek V3.2 (671B parameters) requires approximately 419.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek V3.2?

The recommended quantization for DeepSeek V3.2 is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek V3.2 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek V3.2 achieves approximately 3.3 tokens per second decode speed with a time-to-first-token of 58771ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run DeepSeek V3.2 for coding?

For coding workloads, DeepSeek V3.2 on NVIDIA H100 80GB receives a F grade with 3.3 tok/s and 4K context.

What context window can DeepSeek V3.2 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, DeepSeek V3.2 can safely use up to 4K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V3.2 feels slow on NVIDIA H100 80GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.