Can DeepSeek V3.2 run on NVIDIA H200 PCIe 141GB?

Q: What should I upgrade first if DeepSeek V3.2 feels slow on NVIDIA H200 PCIe 141GB?

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.

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V3.2 needs ~425.1 GB but NVIDIA H200 PCIe 141GB only has 141.0 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) — 425.1 GB, exceeds 141.0 GB available

425.1 GB required141.0 GB available

301% VRAM needed

284.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.7 tok/s

TTFT

41017 ms

Safe context

Memory

425.1 GB / 141.0 GB

Offload

70%

Memory breakdown

Weights409.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V3.2 on NVIDIA H200 PCIe 141GB
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: 4.7 tok/s decode · 41.0s TTFT (warm) · 12 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 425.1 GB, but this setup only exposes 141.0 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	4.7 tok/s	22373 ms	4K
Coding	F	Too heavy	4.7 tok/s	41017 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59661 ms	4K
Reasoning	F	Too heavy	4.7 tok/s	48475 ms	4K
RAG	F	Too heavy	4.7 tok/s	74576 ms	4K

Inference speed

DeepSeek V3.2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek V3.2 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~3 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	3.0	Too big

Quantization options

How DeepSeek V3.2 (671B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 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

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run DeepSeek V3.2?

No, DeepSeek V3.2 requires more memory than NVIDIA H200 PCIe 141GB provides.

How much VRAM does DeepSeek V3.2 need?

DeepSeek V3.2 (671B parameters) requires approximately 425.1 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 H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, DeepSeek V3.2 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41017ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run DeepSeek V3.2 for coding?

For coding workloads, DeepSeek V3.2 on NVIDIA H200 PCIe 141GB receives a F grade with 4.7 tok/s and 4K context.

What context window can DeepSeek V3.2 use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, 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 H200 PCIe 141GB?

See all results for NVIDIA H200 PCIe 141GB See all hardware for DeepSeek V3.2

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