Can DeepSeek Coder V2 16B run on RTX A5000 24GB?

YES — Runs Great

S85Excellent

Estimated from fit model

DeepSeek Coder V2 16B needs ~16.7 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~131 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 16.7 GB, 131.1 tok/s, Runs well

16.7 GB required24.0 GB available

70% VRAM used

Fit status

Runs well

Decode

131.1 tok/s

TTFT

1476 ms

Safe context

52K

Memory

16.7 GB / 24.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on RTX A5000 24GB
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: 131.1 tok/s decode · 1.5s TTFT (warm) · 328 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	131.1 tok/s	805 ms	52K
Coding	S	Runs well	131.1 tok/s	1476 ms	52K
Agentic Coding	A	Tight fit	131.1 tok/s	2147 ms	52K
Reasoning	S	Runs well	131.1 tok/s	1745 ms	52K
RAG	A	Tight fit	131.1 tok/s	2684 ms	52K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A75
Q3_K_S	3	7.8 GB	Low	A76
NVFP4	4	9.0 GB	Medium	A77
Q4_K_M	4	9.8 GB	Medium	A77
Q5_K_M	5	11.5 GB	High	A78
Q6_K	6	13.1 GB	High	A79
Q8_0Best for your GPU	8	17.1 GB	Very High	A78
F16	16	32.8 GB	Maximum	F0

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 RTX A5000 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
Qwen 3.5 27B	27B	S	35.3 tok/s
Qwen 3.6 27B	27B	S	35.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	84.1 tok/s
Qwen 3.5 35B A3B	35B	A	45.5 tok/s

Frequently asked questions

Can RTX A5000 24GB run DeepSeek Coder V2 16B?

Yes, RTX A5000 24GB can run DeepSeek Coder V2 16B with a S grade (Runs well). Expected decode speed: 131.1 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 16.7 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 RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 16B achieves approximately 131.1 tokens per second decode speed with a time-to-first-token of 1476ms using Q4_K_M quantization.

Can RTX A5000 24GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RTX A5000 24GB receives a S grade with 131.1 tok/s and 52K context.

What context window can DeepSeek Coder V2 16B use on RTX A5000 24GB?

On RTX A5000 24GB, DeepSeek Coder V2 16B can safely use up to 52K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A5000 24GB See all hardware for DeepSeek Coder V2 16B

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