Can Qwen 2.5 Coder 3B run on RTX 2060 6GB?

YES — With Offload

A77Great

Estimated from fit model

Qwen 2.5 Coder 3B needs ~5.8 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: 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) — 5.8 GB, 42.0 tok/s, Runs with offload

5.8 GB required6.0 GB available

97% VRAM used

Fit status

Runs with offload

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

17K

Memory

5.8 GB / 6.0 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on RTX 2060 6GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	Runs well	42.0 tok/s	2514 ms	17K
Coding	A	Runs with offload	42.0 tok/s	4610 ms	17K
Agentic Coding	F	Too heavy	42.0 tok/s	6705 ms	17K
Reasoning	A	Runs with offload	42.0 tok/s	5448 ms	17K
RAG	F	Too heavy	42.0 tok/s	8381 ms	17K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A79
Q3_K_S	3	1.5 GB	Low	A80
NVFP4	4	1.7 GB	Medium	A80
Q4_K_M	4	1.8 GB	Medium	A80
Q5_K_M	5	2.2 GB	High	A80
Q6_K	6	2.5 GB	High	A80
Q8_0Best for your GPU	8	3.2 GB	Very High	A79
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.

Run

ollama run qwen2.5-coder:3b

Your hardware

More models your RTX 2060 6GB can run

Model	Params	Grade	Decode
Qwen 3.5 4B	4B	A	52.7 tok/s
Phi-4 Mini Reasoning 4B	3.8B	S	53.2 tok/s
Qwen 3 4B	4B	A	52.7 tok/s
Qwen 2.5 VL 7B	7B	B	25.9 tok/s
Qwen 2.5 7B	7B	B	25.9 tok/s

Frequently asked questions

Can RTX 2060 6GB run Qwen 2.5 Coder 3B?

Yes, RTX 2060 6GB can run Qwen 2.5 Coder 3B with a A grade (Runs with offload). Expected decode speed: 42.0 tok/s.

How much VRAM does Qwen 2.5 Coder 3B need?

Qwen 2.5 Coder 3B (3B parameters) requires approximately 5.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 3B?

The recommended quantization for Qwen 2.5 Coder 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 3B run at on RTX 2060 6GB?

On RTX 2060 6GB, Qwen 2.5 Coder 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can RTX 2060 6GB run Qwen 2.5 Coder 3B for coding?

For coding workloads, Qwen 2.5 Coder 3B on RTX 2060 6GB receives a A grade with 42.0 tok/s and 17K context.

What context window can Qwen 2.5 Coder 3B use on RTX 2060 6GB?

On RTX 2060 6GB, Qwen 2.5 Coder 3B 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 Qwen 2.5 Coder 3B feels slow on RTX 2060 6GB?

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 2060 6GB See all hardware for Qwen 2.5 Coder 3B

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