Can Qwen 2.5 Coder 7B run on NVIDIA A2 16GB?

YES — Runs Great

B70Good

Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 7.9 GB, 39.7 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

39.7 tok/s

TTFT

4881 ms

Safe context

131K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on NVIDIA A2 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: 39.7 tok/s decode · 4.9s TTFT (warm) · 99 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	39.7 tok/s	2662 ms	131K
Coding	B	Runs well	39.7 tok/s	4881 ms	131K
Agentic Coding	A	Runs well	39.7 tok/s	7099 ms	131K
Reasoning	B	Runs well	39.7 tok/s	5768 ms	131K
RAG	A	Runs well	39.7 tok/s	8874 ms	131K

Inference speed

Qwen 2.5 Coder 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	96.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	50.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.2	Fits

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 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B68
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	A71
F16	16	14.3 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5-coder:7b

Upgrade options

Hardware that runs Qwen 2.5 Coder 7B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.71.4 tok/s decode

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+440)

Raises estimated decode speed by about 147%.98 tok/s decode

Raises estimated decode speed by about 147%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Qwen 2.5 Coder 7B?

Yes, NVIDIA A2 16GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 39.7 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.9 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 Coder 7B achieves approximately 39.7 tokens per second decode speed with a time-to-first-token of 4881ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on NVIDIA A2 16GB receives a B grade with 39.7 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, 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 NVIDIA A2 16GB See all hardware for Qwen 2.5 Coder 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-7b-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

39.7 tok/s

2662 ms

131K

Coding

Runs well

39.7 tok/s

4881 ms

131K

Agentic Coding

Runs well

39.7 tok/s

7099 ms

131K

Reasoning

Runs well

39.7 tok/s

5768 ms

131K

RAG

Runs well

39.7 tok/s

8874 ms

131K

Qwen 2.5 Coder 7B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

96.1

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

95.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

95.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

61.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

60.4

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

55.9

Fits

RTX 4060 8GB

8 GB

Q4_K_M

50.5

Tight

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

49.2

Fits

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B68
Q4_K_M	4	4.3 GB	Medium	B68
Q5_K_M	5	5.0 GB	High	B69
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	A71
F16	16	14.3 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A2 16GB run Qwen 2.5 Coder 7B?

Yes, NVIDIA A2 16GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 39.7 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.9 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Qwen 2.5 Coder 7B achieves approximately 39.7 tokens per second decode speed with a time-to-first-token of 4881ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on NVIDIA A2 16GB receives a B grade with 39.7 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, 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.