Can Qwen 2.5 Coder 7B run on RTX 4070 12GB?

YES — Runs Great

A75Great
Estimated from fit model

Qwen 2.5 Coder 7B needs ~7.5 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) 7.5 GB, 96.1 tok/s, Runs well
7.5 GB required12.0 GB available
63% VRAM used

Fit status

Runs well

Decode

96.1 tok/s

TTFT

2014 ms

Safe context

100K

Memory

7.5 GB / 12.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on RTX 4070 12GB
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: 96.1 tok/s decode · 2.0s TTFT (warm) · 240 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well96.1 tok/s1099 ms100K
CodingARuns well96.1 tok/s2014 ms100K
Agentic CodingARuns well96.1 tok/s2930 ms100K
ReasoningARuns well96.1 tok/s2381 ms100K
RAGARuns well96.1 tok/s3662 ms100K

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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M98.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M98.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M98.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M98.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M98.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M98.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M98.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M98.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M96.1Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M95.3Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M95.3Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M61.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M60.4Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M55.9Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M50.5Tight
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M49.2Fits

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 RTX 4070 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB69
Q3_K_S
3
3.4 GB
LowB70
NVFP4
4
3.9 GB
MediumA70
Q4_K_M
4
4.3 GB
MediumA71
Q5_K_M
5
5.0 GB
HighA72
Q6_K
6
5.7 GB
HighA72
Q8_0Best for your GPU
8
7.5 GB
Very HighA72
F16
16
14.3 GB
MaximumF0

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 4070 12GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 9B9BS74 tok/s
AlibabaQwen 3 14B14BA28.5 tok/s
AlibabaQwen 3 8B8BS83.3 tok/s
NVIDIANemotron Nano 8B8BS83.3 tok/s
MistralMinistral 3 14B14BA28.4 tok/s

Frequently asked questions

Can RTX 4070 12GB run Qwen 2.5 Coder 7B?

Yes, RTX 4070 12GB can run Qwen 2.5 Coder 7B with a A grade (Runs well). Expected decode speed: 96.1 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 7.5 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 4070 12GB?

On RTX 4070 12GB, Qwen 2.5 Coder 7B achieves approximately 96.1 tokens per second decode speed with a time-to-first-token of 2014ms using Q4_K_M quantization.

Can RTX 4070 12GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on RTX 4070 12GB receives a A grade with 96.1 tok/s and 100K context.

What context window can Qwen 2.5 Coder 7B use on RTX 4070 12GB?

On RTX 4070 12GB, Qwen 2.5 Coder 7B can safely use up to 100K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4070 12GBSee all hardware for Qwen 2.5 Coder 7B
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