Can DeepSeek Coder V2 16B run on Tesla P40 24GB?

YES — Runs Great

A83Great

Estimated from fit model

DeepSeek Coder V2 16B needs ~16.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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, 49.8 tok/s, Runs well

16.7 GB required24.0 GB available

70% VRAM used

Fit status

Runs well

Decode

49.8 tok/s

TTFT

3888 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 Tesla P40 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: 49.8 tok/s decode · 3.9s TTFT (warm) · 125 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	49.8 tok/s	2120 ms	52K
Coding	A	Runs well	49.8 tok/s	3888 ms	52K
Agentic Coding	A	Tight fit	49.8 tok/s	5655 ms	52K
Reasoning	A	Runs well	49.8 tok/s	4594 ms	52K
RAG	A	Tight fit	49.8 tok/s	7068 ms	52K

Inference speed

DeepSeek Coder V2 16B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 16B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~293 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	292.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	186.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	168.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	159.8	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	149.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	135.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	113.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	107.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	83.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	83.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	58.5	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	53.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	51.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	40.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	25.5	Too big
RTX 4060 8GB	8 GB	Q4_K_M	9.6	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Tesla P40 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 Tesla P40 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.5 27B	27B	S	13.4 tok/s
Qwen 3.6 27B	27B	S	13.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	31.9 tok/s
Qwen 3.5 35B A3B	35B	A	16.7 tok/s

Frequently asked questions

Can Tesla P40 24GB run DeepSeek Coder V2 16B?

Yes, Tesla P40 24GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 49.8 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 Tesla P40 24GB?

On Tesla P40 24GB, DeepSeek Coder V2 16B achieves approximately 49.8 tokens per second decode speed with a time-to-first-token of 3888ms using Q4_K_M quantization.

Can Tesla P40 24GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Tesla P40 24GB receives a A grade with 49.8 tok/s and 52K context.

What context window can DeepSeek Coder V2 16B use on Tesla P40 24GB?

On Tesla P40 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 Tesla P40 24GB See all hardware for DeepSeek Coder V2 16B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

49.8 tok/s

2120 ms

52K

Coding

Runs well

49.8 tok/s

3888 ms

52K

Agentic Coding

Tight fit

49.8 tok/s

5655 ms

52K

Reasoning

Runs well

49.8 tok/s

4594 ms

52K

RAG

Tight fit

49.8 tok/s

7068 ms

52K

DeepSeek Coder V2 16B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

292.9

Fits

RTX 4090 24GB

24 GB

Q4_K_M

186.9

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

168.6

Fits

RTX 3090 24GB

24 GB

Q4_K_M

159.8

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

149.0

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

135.9

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

113.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

107.3

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

83.9

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

83.9

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

58.5

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

53.7

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

51.3

Fits

RTX 4070 12GB

12 GB

Q4_K_M

40.6

Too big

RTX 3060 12GB

12 GB

Q4_K_M

25.5

Too big

RTX 4060 8GB

8 GB

Q4_K_M

9.6

Too big

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Tesla P40 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

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

30.9 tok/s

Qwen 3.5 27B

27B

13.4 tok/s

Qwen 3.6 27B

27B

13.4 tok/s

Qwen3-VL 30B A3B Instruct

30B

31.9 tok/s

Qwen 3.5 35B A3B

35B

16.7 tok/s

Frequently asked questions

Can Tesla P40 24GB run DeepSeek Coder V2 16B?

Yes, Tesla P40 24GB can run DeepSeek Coder V2 16B with a A grade (Runs well). Expected decode speed: 49.8 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 Tesla P40 24GB?

On Tesla P40 24GB, DeepSeek Coder V2 16B achieves approximately 49.8 tokens per second decode speed with a time-to-first-token of 3888ms using Q4_K_M quantization.

Can Tesla P40 24GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on Tesla P40 24GB receives a A grade with 49.8 tok/s and 52K context.

What context window can DeepSeek Coder V2 16B use on Tesla P40 24GB?

On Tesla P40 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.