Can DeepSeek V3.2 run on NVIDIA B200 180GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V3.2 needs ~429.0 GB but NVIDIA B200 180GB only has 180.0 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) — 429.0 GB, exceeds 180.0 GB available

429.0 GB required180.0 GB available

238% VRAM needed

249.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.7 tok/s

TTFT

18138 ms

Safe context

Memory

429.0 GB / 180.0 GB

Offload

60%

Memory breakdown

Weights409.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V3.2 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: 10.7 tok/s decode · 18.1s TTFT (warm) · 27 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 429.0 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	10.7 tok/s	9885 ms	4K
Coding	F	Too heavy	10.7 tok/s	18138 ms	4K
Agentic Coding	F	Too heavy	10.7 tok/s	26430 ms	4K
Reasoning	F	Too heavy	10.7 tok/s	21436 ms	4K
RAG	F	Too heavy	10.7 tok/s	33038 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
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	2.5	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	2.4	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 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 V3.2 (671B params) fits at each quantization level on NVIDIA B200 180GB (180.0 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 B200 180GB run DeepSeek V3.2?

No, DeepSeek V3.2 requires more memory than NVIDIA B200 180GB provides.

How much VRAM does DeepSeek V3.2 need?

DeepSeek V3.2 (671B parameters) requires approximately 429.0 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 B200 180GB?

On NVIDIA B200 180GB, DeepSeek V3.2 achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18138ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run DeepSeek V3.2 for coding?

For coding workloads, DeepSeek V3.2 on NVIDIA B200 180GB receives a F grade with 10.7 tok/s and 4K context.

What context window can DeepSeek V3.2 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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 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 V3.2

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/deepseek-v3.2-671b-on-b200-180gb" 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

10.7 tok/s

9885 ms

Coding

Too heavy

10.7 tok/s

18138 ms

Agentic Coding

Too heavy

10.7 tok/s

26430 ms

Reasoning

Too heavy

10.7 tok/s

21436 ms

RAG

Too heavy

10.7 tok/s

33038 ms

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

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

3.0

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

2.5

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

2.4

Too big

RTX 5090 32GB

32 GB

Q4_K_M

2.0

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

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

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 B200 180GB run DeepSeek V3.2?

No, DeepSeek V3.2 requires more memory than NVIDIA B200 180GB provides.

How much VRAM does DeepSeek V3.2 need?

DeepSeek V3.2 (671B parameters) requires approximately 429.0 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 B200 180GB?

On NVIDIA B200 180GB, DeepSeek V3.2 achieves approximately 10.7 tokens per second decode speed with a time-to-first-token of 18138ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run DeepSeek V3.2 for coding?

For coding workloads, DeepSeek V3.2 on NVIDIA B200 180GB receives a F grade with 10.7 tok/s and 4K context.

What context window can DeepSeek V3.2 use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, 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 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.