Can Qwen 2.5 Coder 32B run on RTX 3070 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 2.5 Coder 32B needs ~25.4 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 25.4 GB, exceeds 8.0 GB available

25.4 GB required8.0 GB available

318% VRAM needed

17.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.6 tok/s

TTFT

74386 ms

Safe context

Memory

25.4 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 Coder 32B on RTX 3070 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: 2.6 tok/s decode · 74.4s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 25.4 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.4 tok/s	43820 ms	4K
Coding	F	Too heavy	2.6 tok/s	74386 ms	4K
Agentic Coding	F	Too heavy	2.6 tok/s	108199 ms	4K
Reasoning	F	Too heavy	2.6 tok/s	87911 ms	4K
RAG	F	Too heavy	2.6 tok/s	135248 ms	4K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4	17.9 GB	Medium	F0
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

升级选项

能流畅运行 Qwen 2.5 Coder 32B 的硬件

RTX 3090 24GB最佳性价比

24 GB VRAM (+16)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 715%.

~$1,499 MSRP

RTX 4090 24GBNVIDIA 升级

24 GB VRAM (+16)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.24.8 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 854%.

~$1,599 MSRP

RTX 5090 32GB经济之选

32 GB VRAM (+24)1792 GB/s (+1344)

Makes the model fit on the accelerator instead of staying completely out of reach.66.4 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,999 MSRP

Frequently asked questions

Can RTX 3070 8GB run Qwen 2.5 Coder 32B?

No, Qwen 2.5 Coder 32B requires more memory than RTX 3070 8GB provides.

How much VRAM does Qwen 2.5 Coder 32B need?

Qwen 2.5 Coder 32B (32B parameters) requires approximately 25.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 32B?

The recommended quantization for Qwen 2.5 Coder 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 32B run at on RTX 3070 8GB?

On RTX 3070 8GB, Qwen 2.5 Coder 32B achieves approximately 2.6 tokens per second decode speed with a time-to-first-token of 74386ms using Q4_K_M quantization.

Can RTX 3070 8GB run Qwen 2.5 Coder 32B for coding?

For coding workloads, Qwen 2.5 Coder 32B on RTX 3070 8GB receives a F grade with 2.6 tok/s and 4K context.

What context window can Qwen 2.5 Coder 32B use on RTX 3070 8GB?

On RTX 3070 8GB, Qwen 2.5 Coder 32B can safely use up to 4K 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 32B feels slow on RTX 3070 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3070 8GB See all hardware for Qwen 2.5 Coder 32B

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