Can DeepSeek V4 Pro run on NVIDIA B200 180GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V4 Pro needs ~882.8 GB but NVIDIA B200 180GB only has 180.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: 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

NVFP4 (Medium quality) — 882.8 GB, exceeds 180.0 GB available

882.8 GB required180.0 GB available

490% VRAM needed

702.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.0 tok/s

TTFT

48383 ms

Safe context

Memory

882.8 GB / 180.0 GB

Offload

80%

Memory breakdown

Weights862.0 GB

KV Cache1.9 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Pro on NVIDIA B200 180GB
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: 4.0 tok/s decode · 48.4s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 882.8 GB, but this setup only exposes 180.0 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	4.0 tok/s	26391 ms	4K
Coding	F	Too heavy	4.0 tok/s	48383 ms	4K
Agentic Coding	F	Too heavy	4.0 tok/s	70375 ms	4K
Reasoning	F	Too heavy	4.0 tok/s	57180 ms	4K
RAG	F	Too heavy	4.0 tok/s	87969 ms	4K

Quantization options

How DeepSeek V4 Pro (1600B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	624.0 GB	Low	F0
Q3_K_S	3	784.0 GB	Low	F0
NVFP4	4	896.0 GB	Medium	F0
Q4_K_M	4	976.0 GB	Medium	F0
Q5_K_M	5	1152.0 GB	High	F0
Q6_K	6	1312.0 GB	High	F0
Q8_0	8	1712.0 GB	Very High	F0
F16	16	3280.0 GB	Maximum	F0

Frequently asked questions

Can NVIDIA B200 180GB run DeepSeek V4 Pro?

No, DeepSeek V4 Pro requires more memory than NVIDIA B200 180GB provides.

How much VRAM does DeepSeek V4 Pro need?

DeepSeek V4 Pro (1600B parameters) requires approximately 882.8 GB of memory with NVFP4 quantization.

What is the best quantization for DeepSeek V4 Pro?

The recommended quantization for DeepSeek V4 Pro is NVFP4, which balances quality and memory efficiency.

What speed will DeepSeek V4 Pro run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, DeepSeek V4 Pro achieves approximately 4.0 tokens per second decode speed with a time-to-first-token of 48383ms using NVFP4 quantization.

Can NVIDIA B200 180GB run DeepSeek V4 Pro for coding?

For coding workloads, DeepSeek V4 Pro on NVIDIA B200 180GB receives a F grade with 4.0 tok/s and 4K context.

What context window can DeepSeek V4 Pro use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, DeepSeek V4 Pro can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek V4 Pro feels slow on NVIDIA B200 180GB?

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 B200 180GB See all hardware for DeepSeek V4 Pro

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