Can Qwen 2.5 Coder 7B run on GTX 1080 8GB?

YES — Tight Fit

A72Great

Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.1 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 48.0 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

48.0 tok/s

TTFT

4033 ms

Safe context

32K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on GTX 1080 8GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	48.0 tok/s	2200 ms	32K
Coding	A	Tight fit	48.0 tok/s	4033 ms	32K
Agentic Coding	A	Runs with offload	48.0 tok/s	5866 ms	32K
Reasoning	A	Tight fit	48.0 tok/s	4766 ms	32K
RAG	A	Runs with offload	48.0 tok/s	7333 ms	32K

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 GTX 1080 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A73
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4	3.9 GB	Medium	A73
Q4_K_M	4	4.3 GB	Medium	A73
Q5_K_MBest for your GPU	5	5.0 GB	High	A73
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
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

Your hardware

More models your GTX 1080 8GB can run

Model	Params	Grade	Decode
Qwen 3 8B	8B	A	23 tok/s
Nemotron Nano 8B	8B	A	24.4 tok/s
InternVL2 8B	8B	A	24.4 tok/s
Ministral 3 8B	8B	B	23 tok/s
MiniCPM-V 2.6 8B	8B	A	24.4 tok/s

Frequently asked questions

Can GTX 1080 8GB run Qwen 2.5 Coder 7B?

Yes, GTX 1080 8GB can run Qwen 2.5 Coder 7B with a A grade (Tight fit). Expected decode speed: 48.0 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.1 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 GTX 1080 8GB?

On GTX 1080 8GB, Qwen 2.5 Coder 7B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can GTX 1080 8GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on GTX 1080 8GB receives a A grade with 48.0 tok/s and 32K context.

What context window can Qwen 2.5 Coder 7B use on GTX 1080 8GB?

On GTX 1080 8GB, Qwen 2.5 Coder 7B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for GTX 1080 8GB 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-gtx-1080-8gb" 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

Tight fit

48.0 tok/s

2200 ms

32K

Coding

Tight fit

48.0 tok/s

4033 ms

32K

Agentic Coding

Runs with offload

48.0 tok/s

5866 ms

32K

Reasoning

Tight fit

48.0 tok/s

4766 ms

32K

RAG

Runs with offload

48.0 tok/s

7333 ms

32K

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

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A73

Q3_K_S

3.4 GB

Low

A74

NVFP4

3.9 GB

Medium

A73

Q4_K_M

4.3 GB

Medium

A73

Q5_K_MBest for your GPU

5.0 GB

High

A73

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Model

Params

Grade

Decode

Capabilities

23 tok/s

24.4 tok/s

24.4 tok/s

23 tok/s

24.4 tok/s

Frequently asked questions

Can GTX 1080 8GB run Qwen 2.5 Coder 7B?

Yes, GTX 1080 8GB can run Qwen 2.5 Coder 7B with a A grade (Tight fit). Expected decode speed: 48.0 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.1 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 GTX 1080 8GB?

On GTX 1080 8GB, Qwen 2.5 Coder 7B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.

Can GTX 1080 8GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on GTX 1080 8GB receives a A grade with 48.0 tok/s and 32K context.

What context window can Qwen 2.5 Coder 7B use on GTX 1080 8GB?

On GTX 1080 8GB, Qwen 2.5 Coder 7B can safely use up to 32K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.