Can DeepSeek V4 Flash run on AMD Instinct MI250X 128GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek V4 Flash needs ~173.0 GB but AMD Instinct MI250X 128GB only has 128.0 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) — 173.0 GB, exceeds 128.0 GB available

173.0 GB required128.0 GB available

135% VRAM needed

45.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

21.4 tok/s

TTFT

9052 ms

Safe context

Memory

173.0 GB / 128.0 GB

Offload

30%

Memory breakdown

Weights158.0 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V4 Flash on AMD Instinct MI250X 128GB
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: 21.4 tok/s decode · 9.1s TTFT (warm) · 54 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 173.0 GB, but this setup only exposes 128.0 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	21.6 tok/s	4898 ms	4K
Coding	F	Too heavy	21.4 tok/s	9052 ms	4K
Agentic Coding	F	Too heavy	21.0 tok/s	13378 ms	4K
Reasoning	F	Too heavy	21.4 tok/s	10698 ms	4K
RAG	F	Too heavy	21.0 tok/s	16722 ms	4K

Quantization options

How DeepSeek V4 Flash (284B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 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

AMD Instinct MI350X 288GBOpción económica

288 GB VRAM (+160)8000 GB/s (+4800)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.125.8 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.

~$8,000 MSRP

AMD Instinct MI300X 192GBMejor relación calidad-precio

192 GB VRAM (+64)5300 GB/s (+2100)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.89.1 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.

~$15,000 MSRP

AMD Instinct MI325X 256GBMejora AMD

256 GB VRAM (+128)6000 GB/s (+2800)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.94.4 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.

~$20,000 MSRP

Frequently asked questions

Can AMD Instinct MI250X 128GB run DeepSeek V4 Flash?

No, DeepSeek V4 Flash requires more memory than AMD Instinct MI250X 128GB provides.

How much VRAM does DeepSeek V4 Flash need?

DeepSeek V4 Flash (284B parameters) requires approximately 173.0 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 AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, DeepSeek V4 Flash achieves approximately 21.4 tokens per second decode speed with a time-to-first-token of 9052ms using NVFP4 quantization.

Can AMD Instinct MI250X 128GB run DeepSeek V4 Flash for coding?

For coding workloads, DeepSeek V4 Flash on AMD Instinct MI250X 128GB receives a F grade with 21.4 tok/s and 4K context.

What context window can DeepSeek V4 Flash use on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, 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 AMD Instinct MI250X 128GB?

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 AMD Instinct MI250X 128GB See all hardware for DeepSeek V4 Flash

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