Can DeepSeek Coder V2 16B run on Tesla P100 16GB?

YES — With Offload

A82Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~15.9 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~105 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 15.9 GB, 105.4 tok/s, Runs with offload

15.9 GB required16.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

105.4 tok/s

TTFT

1838 ms

Safe context

17K

Memory

15.9 GB / 16.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on Tesla P100 16GB
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: 105.4 tok/s decode · 1.8s TTFT (warm) · 263 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	A	Tight fit	105.4 tok/s	1002 ms	17K
Coding	A	Runs with offload	105.4 tok/s	1838 ms	17K
Agentic Coding	B	Very compromised (needs ~1.6 GB host RAM)	51.8 tok/s	5439 ms	17K
Reasoning	A	Runs with offload	105.4 tok/s	2172 ms	17K
RAG	B	Very compromised (needs ~1.6 GB host RAM)	51.8 tok/s	6798 ms	17K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A79
Q3_K_S	3	7.8 GB	Low	A80
NVFP4	4	9.0 GB	Medium	A80
Q4_K_M	4	9.8 GB	Medium	A80
Q5_K_MBest for your GPU	5	11.5 GB	High	A79
Q6_K	6	13.1 GB	High	F0
Q8_0	8	17.1 GB	Very High	F0
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 Tesla P100 16GB can run

Model	Params	Grade	Decode
GPT-OSS 20B	21B	A	46.4 tok/s
Codestral 2 25.08	22B	A	18 tok/s
CogVLM2 19B	19B	A	26.1 tok/s
Granite Code 20B	20B	B	21.1 tok/s

Frequently asked questions

Can Tesla P100 16GB run DeepSeek Coder V2 16B?

Yes, Tesla P100 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 105.4 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.9 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 Tesla P100 16GB?

On Tesla P100 16GB, DeepSeek Coder V2 16B achieves approximately 105.4 tokens per second decode speed with a time-to-first-token of 1838ms using Q4_K_M quantization.

Can Tesla P100 16GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Tesla P100 16GB receives a A grade with 105.4 tok/s and 17K context.

What context window can DeepSeek Coder V2 16B use on Tesla P100 16GB?

On Tesla P100 16GB, DeepSeek Coder V2 16B can safely use up to 17K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 16B feels slow on Tesla P100 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Tesla P100 16GB See all hardware for DeepSeek Coder V2 16B

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