Can Qwen 2.5 Coder 7B run on RTX 2070 Super 8GB?

YES — Tight Fit

A73Great

○Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.1 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~70 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, 69.5 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

69.5 tok/s

TTFT

2786 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 RTX 2070 Super 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: 69.5 tok/s decode · 2.8s TTFT (warm) · 174 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	69.5 tok/s	1520 ms	32K
Coding	A	Tight fit	69.5 tok/s	2786 ms	32K
Agentic Coding	A	Runs with offload	69.5 tok/s	4053 ms	32K
Reasoning	A	Tight fit	69.5 tok/s	3293 ms	32K
RAG	A	Runs with offload	69.5 tok/s	5066 ms	32K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on RTX 2070 Super 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 RTX 2070 Super 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 8B	8B	A	33.3 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	35.3 tok/s
👁 InternLM InternVL2 8B	8B	A	35.3 tok/s
👁 Mistral Ministral 3 8B	8B	A	33.3 tok/s
👁 OpenBMB MiniCPM-V 2.6 8B	8B	A	35.3 tok/s

Frequently asked questions

See all results for RTX 2070 Super 8GB See all hardware for Qwen 2.5 Coder 7B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-7b-on-rtx-2070-super-8gb

⇱ Qwen 2.5 Coder 7B on RTX 2070 Super 8GB? TIGHT FIT