Can DeepSeek Coder V2 16B run on NVIDIA V100 32GB?

YES — Runs Great

A82Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~17.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~147 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 17.5 GB, 147.1 tok/s, Runs well

17.5 GB required32.0 GB available

55% VRAM used

Fit status

Runs well

Decode

147.1 tok/s

TTFT

1316 ms

Safe context

87K

Memory

17.5 GB / 32.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on NVIDIA V100 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: 147.1 tok/s decode · 1.3s TTFT (warm) · 368 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	147.1 tok/s	718 ms	87K
Coding	A	Runs well	147.1 tok/s	1316 ms	87K
Agentic Coding	A	Runs well	147.1 tok/s	1914 ms	87K
Reasoning	A	Runs well	147.1 tok/s	1555 ms	87K
RAG	A	Runs well	147.1 tok/s	2393 ms	87K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on NVIDIA V100 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	9.0 GB	Medium	A74
Q4_K_M	4	9.8 GB	Medium	A74
Q5_K_M	5	11.5 GB	High	A75
Q6_K	6	13.1 GB	High	A76
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 NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run DeepSeek Coder V2 16B?

Yes, NVIDIA V100 32GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 147.1 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 17.5 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek Coder V2 16B achieves approximately 147.1 tokens per second decode speed with a time-to-first-token of 1316ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on NVIDIA V100 32GB receives a A grade with 147.1 tok/s and 87K context.

What context window can DeepSeek Coder V2 16B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek Coder V2 16B can safely use up to 87K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for DeepSeek Coder V2 16B

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