Can DeepSeek V4 Flash run on NVIDIA H100 PCIe 80GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V4 Flash needs ~169.3 GB but NVIDIA H100 PCIe 80GB only has 80.0 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) — 168.2 GB, exceeds 80.0 GB available

168.2 GB required80.0 GB available

210% VRAM needed

88.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.1 tok/s

TTFT

31859 ms

Safe context

Memory

168.2 GB / 80.0 GB

Offload

50%

Memory breakdown

Weights158.0 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Flash on NVIDIA H100 PCIe 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: 6.1 tok/s decode · 31.9s TTFT (warm) · 15 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 169.3 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	5.5 tok/s	19177 ms	4K
Coding	F	Too heavy	5.5 tok/s	35438 ms	4K
Agentic Coding	F	Too heavy	5.4 tok/s	52364 ms	4K
Reasoning	F	Too heavy	5.5 tok/s	41881 ms	4K
RAG	F	Too heavy	5.4 tok/s	65455 ms	4K

Inference speed

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

Estimated decode speed (tokens/sec) for DeepSeek V4 Flash at NVFP4 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 ~18 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	NVFP4	17.8	Offloads
Mac Studio M2 Ultra 128GB	128 GB	NVFP4	7.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	NVFP4	6.7	Too big
MacBook Pro M4 Max 128GB	128 GB	NVFP4	5.2	Too big
MacBook Pro M4 Max 64GB	64 GB	NVFP4	5.2	Too big
RTX 5090 32GB	32 GB	NVFP4	3.9	Too big
2× RX 7900 XTX 24GB	48 GB	NVFP4	3.8	Too big
MacBook Pro M3 Max 64GB	64 GB	NVFP4	3.6	Too big
MacBook Pro M1 Max 64GB	64 GB	NVFP4	3.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	NVFP4	3.2	Too big
2× RTX 4090 24GB	48 GB	NVFP4	2.6	Too big
RTX 4090 24GB	24 GB	NVFP4	2.5	Too big
2× RTX 3090 24GB	48 GB	NVFP4	2.3	Too big
RX 7900 XTX 24GB	24 GB	NVFP4	2.2	Too big
RTX 3090 24GB	24 GB	NVFP4	2.1	Too big
RTX 4080 Super 16GB	16 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
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 Flash (284B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	110.8 GB	Low	F0
Q3_K_S	3	139.2 GB	Low	F0
NVFP4	4	159.0 GB	Medium	F0
Q4_K_M	4	173.2 GB	Medium	F0
Q5_K_M	5	204.5 GB	High	F0
Q6_K	6	232.9 GB	High	F0
Q8_0	8	303.9 GB	Very High	F0
F16	16	582.2 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien DeepSeek V4 Flash

NVIDIA B200 180GBOpción económica

180 GB VRAM (+100)8000 GB/s (+6000)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.144.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$30,000 MSRP

B100 192GBMejor relación calidad-precio

192 GB VRAM (+112)8000 GB/s (+6000)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.144.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$35,000 MSRP

NVIDIA GB200 192GBMejora NVIDIA

192 GB VRAM (+112)8000 GB/s (+6000)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.144.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$60,000 MSRP

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run DeepSeek V4 Flash?

No, DeepSeek V4 Flash requires more memory than NVIDIA H100 PCIe 80GB provides.

How much VRAM does DeepSeek V4 Flash need?

DeepSeek V4 Flash (284B parameters) requires approximately 169.3 GB of memory with NVFP4 quantization.

What is the best quantization for DeepSeek V4 Flash?

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

What speed will DeepSeek V4 Flash run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, DeepSeek V4 Flash achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 35438ms using NVFP4 quantization.

Can NVIDIA H100 PCIe 80GB run DeepSeek V4 Flash for coding?

For coding workloads, DeepSeek V4 Flash on NVIDIA H100 PCIe 80GB receives a F grade with 5.5 tok/s and 4K context.

What context window can DeepSeek V4 Flash use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, DeepSeek V4 Flash 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 Flash feels slow on NVIDIA H100 PCIe 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 PCIe 80GB See all hardware for DeepSeek V4 Flash

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