Can Qwen 2.5 Coder 14B run on RTX 3500 Ada Laptop 12GB?

BARELY — Tight on Memory

C53Usable

Estimated from fit model

Qwen 2.5 Coder 14B needs ~13.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 13.6 GB, 18.9 tok/s, Very compromised (needs ~1 GB host RAM)

13.6 GB required12.0 GB available

113% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

18.9 tok/s

TTFT

10266 ms

Safe context

Memory

13.6 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on RTX 3500 Ada Laptop 12GB
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: 18.9 tok/s decode · 10.3s TTFT (warm) · 47 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload (needs ~0.1 GB host RAM)	24.0 tok/s	4403 ms	7K
Coding	C	Very compromised (needs ~1 GB host RAM)	18.9 tok/s	10266 ms	7K
Agentic Coding	F	Too heavy	12.5 tok/s	22534 ms	7K
Reasoning	C	Very compromised (needs ~1 GB host RAM)	18.9 tok/s	12133 ms	7K
RAG	F	Too heavy	12.5 tok/s	28168 ms	7K

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 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B67
Q3_K_S	3	6.9 GB	Low	B66
NVFP4	4	7.8 GB	Medium	B66
Q4_K_MBest for your GPU	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
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

Opções de upgrade

Hardware que roda bem Qwen 2.5 Coder 14B

RTX 5060 Ti 16GBOpção econômica

16 GB VRAM (+4)448 GB/s (+112)

Removes host-memory offload, which is usually the single biggest latency and throughput win.34.1 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 80%.

~$449 MSRP

RTX 4060 Ti 16GBMelhor custo-benefício

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.27.9 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 48%.

~$499 MSRP

RTX 5070 Ti 16GBUpgrade NVIDIA

16 GB VRAM (+4)896 GB/s (+560)

Removes host-memory offload, which is usually the single biggest latency and throughput win.70.3 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 272%.

~$749 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Qwen 2.5 Coder 14B?

Yes, RTX 3500 Ada Laptop 12GB can run Qwen 2.5 Coder 14B with a C grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 18.9 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 13.6 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Qwen 2.5 Coder 14B achieves approximately 18.9 tokens per second decode speed with a time-to-first-token of 10266ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RTX 3500 Ada Laptop 12GB receives a C grade with 18.9 tok/s and 7K context.

What context window can Qwen 2.5 Coder 14B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Qwen 2.5 Coder 14B can safely use up to 7K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Coder 14B feels slow on RTX 3500 Ada Laptop 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Qwen 2.5 Coder 14B

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