Can Qwen 2.5 Coder 14B run on RTX 6000 Ada Laptop 16GB?

YES — Tight Fit

B67Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~14.0 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.0 GB, 55.8 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

55.8 tok/s

TTFT

3467 ms

Safe context

27K

Memory

14.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on RTX 6000 Ada 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: 55.8 tok/s decode · 3.5s TTFT (warm) · 140 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	B	Runs well	55.8 tok/s	1891 ms	27K
Coding	B	Tight fit	55.8 tok/s	3467 ms	27K
Agentic Coding	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	7545 ms	27K
Reasoning	B	Tight fit	55.8 tok/s	4098 ms	27K
RAG	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	9431 ms	27K

Inference speed

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 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	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	81.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	29.1	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	17.0	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.3	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 Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B64
Q3_K_S	3	6.9 GB	Low	B65
NVFP4	4	7.8 GB	Medium	B66
Q4_K_M	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	B65
Q6_KBest for your GPU	6	11.5 GB	High	B65
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.35.5 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Raises estimated decode speed by about 46%.81.2 tok/s decode

Raises estimated decode speed by about 46%.

Adds memory headroom for longer context windows and future model growth.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Raises estimated decode speed by about 82%.101.7 tok/s decode

Raises estimated decode speed by about 82%.

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

Frequently asked questions

Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 14B?

Yes, RTX 6000 Ada Laptop 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 55.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 14B achieves approximately 55.8 tokens per second decode speed with a time-to-first-token of 3467ms using Q4_K_M quantization.

Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RTX 6000 Ada Laptop 16GB receives a B grade with 55.8 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 14B can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada Laptop 16GB See all hardware for Qwen 2.5 Coder 14B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

55.8 tok/s

1891 ms

27K

Coding

Tight fit

55.8 tok/s

3467 ms

27K

Agentic Coding

Runs with offload (needs ~0.5 GB host RAM)

37.3 tok/s

7545 ms

27K

Reasoning

Tight fit

55.8 tok/s

4098 ms

27K

RAG

Runs with offload (needs ~0.5 GB host RAM)

37.3 tok/s

9431 ms

27K

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

151.8

Fits

RTX 4090 24GB

24 GB

Q4_K_M

96.9

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

87.4

Fits

RTX 3090 24GB

24 GB

Q4_K_M

82.9

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

81.1

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

70.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

58.7

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

55.6

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

38.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

38.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

30.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

29.1

Heavy offload

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

27.8

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

23.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

17.0

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

6.3

Too big

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B64
Q3_K_S	3	6.9 GB	Low	B65
NVFP4	4	7.8 GB	Medium	B66
Q4_K_M	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	B65
Q6_KBest for your GPU	6	11.5 GB	High	B65
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Frequently asked questions

Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 14B?

Yes, RTX 6000 Ada Laptop 16GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 55.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 14B achieves approximately 55.8 tokens per second decode speed with a time-to-first-token of 3467ms using Q4_K_M quantization.

Can RTX 6000 Ada Laptop 16GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RTX 6000 Ada Laptop 16GB receives a B grade with 55.8 tok/s and 27K context.

What context window can Qwen 2.5 Coder 14B use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, Qwen 2.5 Coder 14B can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.