Can DeepSeek V4 Flash run on RTX 5090 32GB?

Q: What should I upgrade first if DeepSeek V4 Flash feels slow on RTX 5090 32GB?

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 V4 Flash needs ~163.4 GB but RTX 5090 32GB only has 32.0 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) — 163.4 GB, exceeds 32.0 GB available

163.4 GB required32.0 GB available

511% VRAM needed

131.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.9 tok/s

TTFT

49898 ms

Safe context

Memory

163.4 GB / 32.0 GB

Offload

80%

Memory breakdown

Weights158.0 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Flash on RTX 5090 32GB
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: 3.9 tok/s decode · 49.9s TTFT (warm) · 10 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 163.4 GB, but this setup only exposes 32.0 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	3.9 tok/s	27217 ms	4K
Coding	F	Too heavy	3.9 tok/s	49898 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	72579 ms	4K
Reasoning	F	Too heavy	3.9 tok/s	58970 ms	4K
RAG	F	Too heavy	3.9 tok/s	90724 ms	4K

Quantization options

How DeepSeek V4 Flash (284B params) fits at each quantization level on RTX 5090 32GB (32.0 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

Upgrade options

Hardware that runs DeepSeek V4 Flash well

NVIDIA B200 180GBBudget pick

180 GB VRAM (+148)8000 GB/s (+6208)

Makes the model fit on the accelerator instead of staying completely out of reach.144.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$30,000 MSRP

B100 192GBBest value

192 GB VRAM (+160)8000 GB/s (+6208)

Makes the model fit on the accelerator instead of staying completely out of reach.144.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$35,000 MSRP

NVIDIA GB200 192GBNVIDIA upgrade

192 GB VRAM (+160)8000 GB/s (+6208)

Makes the model fit on the accelerator instead of staying completely out of reach.144.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$60,000 MSRP

Frequently asked questions

Can RTX 5090 32GB run DeepSeek V4 Flash?

No, DeepSeek V4 Flash requires more memory than RTX 5090 32GB provides.

How much VRAM does DeepSeek V4 Flash need?

DeepSeek V4 Flash (284B parameters) requires approximately 163.4 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 RTX 5090 32GB?

On RTX 5090 32GB, DeepSeek V4 Flash achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49898ms using NVFP4 quantization.

Can RTX 5090 32GB run DeepSeek V4 Flash for coding?

For coding workloads, DeepSeek V4 Flash on RTX 5090 32GB receives a F grade with 3.9 tok/s and 4K context.

What context window can DeepSeek V4 Flash use on RTX 5090 32GB?

On RTX 5090 32GB, 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 RTX 5090 32GB?

See all results for RTX 5090 32GB See all hardware for DeepSeek V4 Flash

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