Can DeepSeek Coder V2 16B run on RTX 4090 Laptop 16GB?

YES — With Offload

A82Great

Estimated — low-sample bucket· few comparable runs

DeepSeek Coder V2 16B needs ~15.6 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~101 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 15.6 GB, 101.2 tok/s, Runs with offload

15.6 GB required16.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

101.2 tok/s

TTFT

1914 ms

Safe context

18K

Memory

15.6 GB / 16.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on RTX 4090 Laptop 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: 101.2 tok/s decode · 1.9s TTFT (warm) · 253 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.

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	101.2 tok/s	1044 ms	18K
Coding	A	Runs with offload	101.2 tok/s	1914 ms	18K
Agentic Coding	B	Very compromised (needs ~1.5 GB host RAM)	53.7 tok/s	5242 ms	18K
Reasoning	A	Runs with offload	101.2 tok/s	2262 ms	18K
RAG	B	Very compromised (needs ~1.5 GB host RAM)	53.7 tok/s	6552 ms	18K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 4090 Laptop 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 RTX 4090 Laptop 16GB can run

Model	Params	Grade	Decode
GPT-OSS 20B	21B	A	48 tok/s
Codestral 2 25.08	22B	A	14.1 tok/s
CogVLM2 19B	19B	A	25 tok/s
Granite Code 20B	20B	B	19.6 tok/s

Frequently asked questions

Can RTX 4090 Laptop 16GB run DeepSeek Coder V2 16B?

Yes, RTX 4090 Laptop 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 101.2 tok/s.

How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 15.6 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 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, DeepSeek Coder V2 16B achieves approximately 101.2 tokens per second decode speed with a time-to-first-token of 1914ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run DeepSeek Coder V2 16B for coding?

For coding workloads, DeepSeek Coder V2 16B on RTX 4090 Laptop 16GB receives a A grade with 101.2 tok/s and 18K context.

What context window can DeepSeek Coder V2 16B use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, DeepSeek Coder V2 16B can safely use up to 18K 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 RTX 4090 Laptop 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 RTX 4090 Laptop 16GB See all hardware for DeepSeek Coder V2 16B

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Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	101.2 tok/s	1044 ms	18K
Coding	A	Runs with offload	101.2 tok/s	1914 ms	18K
Agentic Coding	B	Very compromised (needs ~1.5 GB host RAM)	53.7 tok/s	5242 ms	18K
Reasoning	A	Runs with offload	101.2 tok/s	2262 ms	18K
RAG	B	Very compromised (needs ~1.5 GB host RAM)	53.7 tok/s	6552 ms	18K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 4090 Laptop 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

Model

Params

Grade

Decode

Capabilities

GPT-OSS 20B

21B

48 tok/s

Codestral 2 25.08

22B

14.1 tok/s

CogVLM2 19B

19B

25 tok/s

Granite Code 20B

20B

19.6 tok/s