Can DeepSeek Coder V2 16B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A82Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~17.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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

Q4_K_M (Medium quality) — 17.2 GB, 92.1 tok/s, Runs well

17.2 GB required32.0 GB available

54% VRAM used

Fit status

Runs well

Decode

92.1 tok/s

TTFT

2102 ms

Safe context

88K

Memory

17.2 GB / 32.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on Radeon AI PRO R9700 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: 92.1 tok/s decode · 2.1s TTFT (warm) · 230 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	92.1 tok/s	1146 ms	88K
Coding	A	Runs well	92.1 tok/s	2102 ms	88K
Agentic Coding	A	Runs well	92.1 tok/s	3057 ms	88K
Reasoning	A	Runs well	92.1 tok/s	2484 ms	88K
RAG	A	Runs well	92.1 tok/s	3821 ms	88K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A73
Q3_K_S	3	7.8 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.5 27B	27B	S	24.8 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run DeepSeek Coder V2 16B?

Yes, Radeon AI PRO R9700 32GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 92.1 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 17.2 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 16B run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, DeepSeek Coder V2 16B achieves approximately 92.1 tokens per second decode speed with a time-to-first-token of 2102ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Radeon AI PRO R9700 32GB receives a A grade with 92.1 tok/s and 88K context.

What context window can DeepSeek Coder V2 16B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, DeepSeek Coder V2 16B can safely use up to 88K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for DeepSeek Coder V2 16B

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