Can Qwen 2.5 Coder 7B run on RTX 4000 Ada 20GB?

YES — Runs Great

B70Good

Estimated from fit model

Qwen 2.5 Coder 7B needs ~8.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~71 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) — 8.3 GB, 71.4 tok/s, Runs well

8.3 GB required20.0 GB available

42% VRAM used

Fit status

Runs well

Decode

71.4 tok/s

TTFT

2712 ms

Safe context

131K

Memory

8.3 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on RTX 4000 Ada 20GB
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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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	71.4 tok/s	1479 ms	131K
Coding	B	Runs well	71.4 tok/s	2712 ms	131K
Agentic Coding	A	Runs well	71.4 tok/s	3944 ms	131K
Reasoning	B	Runs well	71.4 tok/s	3205 ms	131K
RAG	A	Runs well	71.4 tok/s	4930 ms	131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B66
NVFP4	4	3.9 GB	Medium	B66
Q4_K_M	4	4.3 GB	Medium	B66
Q5_K_M	5	5.0 GB	High	B67
Q6_K	6	5.7 GB	High	B67
Q8_0	8	7.5 GB	Very High	B69
F16Best for your GPU	16	14.3 GB	Maximum	A70

Get started

Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Frequently asked questions

Can RTX 4000 Ada 20GB run Qwen 2.5 Coder 7B?

Yes, RTX 4000 Ada 20GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 71.4 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Qwen 2.5 Coder 7B achieves approximately 71.4 tokens per second decode speed with a time-to-first-token of 2712ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on RTX 4000 Ada 20GB receives a B grade with 71.4 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Qwen 2.5 Coder 7B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Qwen 2.5 Coder 7B

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