Can DeepSeek V4 Pro run on H100 NVL 188GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V4 Pro needs ~883.6 GB but H100 NVL 188GB only has 188.0 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) — 883.6 GB, exceeds 188.0 GB available

883.6 GB required188.0 GB available

470% VRAM needed

695.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.8 tok/s

TTFT

51462 ms

Safe context

Memory

883.6 GB / 188.0 GB

Offload

80%

Memory breakdown

Weights862.0 GB

KV Cache1.9 GB

Runtime0.9 GB

Headroom18.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Pro on H100 NVL 188GB
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: 3.8 tok/s decode · 51.5s TTFT (warm) · 9 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 883.6 GB, but this setup only exposes 188.0 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	3.8 tok/s	28070 ms	4K
Coding	F	Too heavy	3.8 tok/s	51462 ms	4K
Agentic Coding	F	Too heavy	3.8 tok/s	74853 ms	4K
Reasoning	F	Too heavy	3.8 tok/s	60818 ms	4K
RAG	F	Too heavy	3.8 tok/s	93567 ms	4K

Inference speed

DeepSeek V4 Pro inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek V4 Pro at NVFP4 across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 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?
RTX 5090 32GB	32 GB	NVFP4	2.0	Too big
RTX 4090 24GB	24 GB	NVFP4	2.0	Too big
RTX 4080 Super 16GB	16 GB	NVFP4	2.0	Too big
RTX 3090 24GB	24 GB	NVFP4	2.0	Too big
RTX 4070 12GB	12 GB	NVFP4	2.0	Too big
RTX 3060 12GB	12 GB	NVFP4	2.0	Too big
RTX 4060 8GB	8 GB	NVFP4	2.0	Too big
RX 7900 XTX 24GB	24 GB	NVFP4	2.0	Too big
MacBook Pro M4 Max 128GB	128 GB	NVFP4	2.0	Too big
Mac Studio M3 Ultra 256GB	256 GB	NVFP4	2.0	Too big
Mac Studio M2 Ultra 128GB	128 GB	NVFP4	2.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	NVFP4	2.0	Too big
MacBook Pro M4 Max 64GB	64 GB	NVFP4	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	NVFP4	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	NVFP4	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	NVFP4	2.0	Too big
2× RTX 4090 24GB	48 GB	NVFP4	2.0	Too big
2× RTX 3090 24GB	48 GB	NVFP4	2.0	Too big
2× RX 7900 XTX 24GB	48 GB	NVFP4	2.0	Too big
4× RTX 3060 12GB	48 GB	NVFP4	2.0	Too big

Estimates for single-stream decoding at NVFP4; 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 V4 Pro (1600B params) fits at each quantization level on H100 NVL 188GB (188.0 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 H100 NVL 188GB run DeepSeek V4 Pro?

No, DeepSeek V4 Pro requires more memory than H100 NVL 188GB provides.

How much VRAM does DeepSeek V4 Pro need?

DeepSeek V4 Pro (1600B parameters) requires approximately 883.6 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 H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek V4 Pro achieves approximately 3.8 tokens per second decode speed with a time-to-first-token of 51462ms using NVFP4 quantization.

Can H100 NVL 188GB run DeepSeek V4 Pro for coding?

For coding workloads, DeepSeek V4 Pro on H100 NVL 188GB receives a F grade with 3.8 tok/s and 4K context.

What context window can DeepSeek V4 Pro use on H100 NVL 188GB?

On H100 NVL 188GB, 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 H100 NVL 188GB?

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 H100 NVL 188GB See all hardware for DeepSeek V4 Pro

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

3.8 tok/s

28070 ms

Coding

Too heavy

3.8 tok/s

51462 ms

Agentic Coding

Too heavy

3.8 tok/s

74853 ms

Reasoning

Too heavy

3.8 tok/s

60818 ms

RAG

Too heavy

3.8 tok/s

93567 ms

DeepSeek V4 Pro inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

NVFP4

2.0

Too big

RTX 4090 24GB

24 GB

NVFP4

2.0

Too big

RTX 4080 Super 16GB

16 GB

NVFP4

2.0

Too big

RTX 3090 24GB

24 GB

NVFP4

2.0

Too big

RTX 4070 12GB

12 GB

NVFP4

2.0

Too big

RTX 3060 12GB

12 GB

NVFP4

2.0

Too big

RTX 4060 8GB

8 GB

NVFP4

2.0

Too big

RX 7900 XTX 24GB

24 GB

NVFP4

2.0

Too big

MacBook Pro M4 Max 128GB

128 GB

NVFP4

2.0

Too big

Mac Studio M3 Ultra 256GB

256 GB

NVFP4

2.0

Too big

Mac Studio M2 Ultra 128GB

128 GB

NVFP4

2.0

Too big

Mac Studio M1 Ultra 128GB

128 GB

NVFP4

2.0

Too big

MacBook Pro M4 Max 64GB

64 GB

NVFP4

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

NVFP4

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

NVFP4

2.0

Too big

MacBook Pro M4 Pro 48GB

48 GB

NVFP4

2.0

Too big

2× RTX 4090 24GB

48 GB

NVFP4

2.0

Too big

2× RTX 3090 24GB

48 GB

NVFP4

2.0

Too big

2× RX 7900 XTX 24GB

48 GB

NVFP4

2.0

Too big

4× RTX 3060 12GB

48 GB

NVFP4

2.0

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

624.0 GB

Low

Q3_K_S

784.0 GB

Low

NVFP4

896.0 GB

Medium

Q4_K_M

976.0 GB

Medium

Q5_K_M

1152.0 GB

High

Q6_K

1312.0 GB

High

Q8_0

1712.0 GB

Very High

F16

3280.0 GB

Maximum

Frequently asked questions

Can H100 NVL 188GB run DeepSeek V4 Pro?

No, DeepSeek V4 Pro requires more memory than H100 NVL 188GB provides.

How much VRAM does DeepSeek V4 Pro need?

DeepSeek V4 Pro (1600B parameters) requires approximately 883.6 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 H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek V4 Pro achieves approximately 3.8 tokens per second decode speed with a time-to-first-token of 51462ms using NVFP4 quantization.

Can H100 NVL 188GB run DeepSeek V4 Pro for coding?

For coding workloads, DeepSeek V4 Pro on H100 NVL 188GB receives a F grade with 3.8 tok/s and 4K context.

What context window can DeepSeek V4 Pro use on H100 NVL 188GB?

On H100 NVL 188GB, 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 H100 NVL 188GB?

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.