Can DeepSeek V3.2 run on AMD Instinct MI350X 288GB?

YES — With Q2_K

S90Excellent

○Estimated from fit model

DeepSeek V3.2 needs ~291.9 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q2_K quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

DeepSeek V3.2 at Q4_K_M needs 439.5 GB — too much for AMD Instinct MI350X 288GB (288.0 GB). Runs at Q2_K (291.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 439.5 GB, exceeds 288.0 GB available

439.5 GB required288.0 GB available

153% VRAM needed

151.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.0 tok/s

TTFT

13787 ms

Safe context

Memory

439.5 GB / 288.0 GB

Offload

30%

Memory breakdown

Weights409.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek V3.2 on AMD Instinct MI350X 288GB
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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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	F	Too heavy	14.1 tok/s	7512 ms	4K
Coding	F	Too heavy	12.8 tok/s	15154 ms	4K
Agentic Coding	F	Too heavy	14.0 tok/s	20098 ms	4K
Reasoning	F	Too heavy	12.8 tok/s	17910 ms	4K
RAG	F	Too heavy	12.7 tok/s	27615 ms	4K

Quantization options

How DeepSeek V3.2 (671B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	261.7 GB	Low	F0
Q3_K_S	3	328.8 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run DeepSeek V3.2 on your machine.

Run

ollama run deepseek-v3.2

Frequently asked questions

See all results for AMD Instinct MI350X 288GB See all hardware for DeepSeek V3.2

URL: https://willitrunai.com/can-run/deepseek-v3.2-671b-on-instinct-mi350x-288gb

⇱