Can DeepSeek V4 Flash run on B100 192GB?

YES — Tight Fit

S96Excellent

○Estimated from fit model

DeepSeek V4 Flash needs ~179.4 GB VRAM. B100 192GB has 192.0 GB. With NVFP4 quantization, expect ~145 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced

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) — 179.4 GB, 144.8 tok/s, Tight fit

179.4 GB required192.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

144.8 tok/s

TTFT

1337 ms

Safe context

169K

Memory

179.4 GB / 192.0 GB

Memory breakdown

Weights158.0 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsDeepSeek V4 Flash on B100 192GB
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: 144.8 tok/s decode · 1.3s TTFT (warm) · 362 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	144.8 tok/s	729 ms	169K
Coding	S	Tight fit	144.8 tok/s	1337 ms	169K
Agentic Coding	S	Tight fit	144.8 tok/s	1945 ms	169K
Reasoning	S	Tight fit	144.8 tok/s	1580 ms	169K
RAG	S	Tight fit	144.8 tok/s	2431 ms	169K

Quantization options

How DeepSeek V4 Flash (284B params) fits at each quantization level on B100 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	110.8 GB	Low	S90
Q3_K_SBest for your GPU	3	139.2 GB	Low	S90

Get started

Copy-paste commands to run DeepSeek V4 Flash on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "deepseek-ai/DeepSeek-V4-Flash" \
 --hf-file "DeepSeek-V4-Flash-NVFP4.gguf" \
 -c 4096 -ngl 99

Frequently asked questions

See all results for B100 192GB See all hardware for DeepSeek V4 Flash

URL: https://willitrunai.com/can-run/deepseek-v4-flash-on-b100-192gb

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