Can Qwen 2.5 Math 7B run on RTX 4060 Ti 8GB?

YES — Tight Fit

B56Good

Estimated from fit model

Qwen 2.5 Math 7B needs ~7.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 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, 53.5 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

53.5 tok/s

TTFT

3622 ms

Safe context

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 Math 7B on RTX 4060 Ti 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 53.5 tok/s decode · 3.6s TTFT (warm) · 134 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	Tight fit	49.2 tok/s	2145 ms	4K
Coding	B	Tight fit	49.2 tok/s	3932 ms	4K
Agentic Coding	B	Runs with offload	49.2 tok/s	5719 ms	4K
Reasoning	B	Tight fit	49.2 tok/s	4647 ms	4K
RAG	B	Runs with offload	49.2 tok/s	7149 ms	4K

Quantization options

How Qwen 2.5 Math 7B (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B57
Q3_K_S	3	3.4 GB	Low	B58
NVFP4	4

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen2.5-Math-7B-Instruct" \
  --hf-file "Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen 2.5 Math 7B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+72)

Adds memory headroom for longer context windows and future model growth.60.4 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+384)

Raises estimated decode speed by about 83%.98 tok/s decode

Raises estimated decode speed by about 83%.

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

~$549 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+216)

Raises estimated decode speed by about 80%.96.1 tok/s decode

Raises estimated decode speed by about 80%.

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

~$599 MSRP

Frequently asked questions

Can RTX 4060 Ti 8GB run Qwen 2.5 Math 7B?

Yes, RTX 4060 Ti 8GB can run Qwen 2.5 Math 7B with a B grade (Tight fit). Expected decode speed: 49.2 tok/s.

How much VRAM does Qwen 2.5 Math 7B need?

Qwen 2.5 Math 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Math 7B?

The recommended quantization for Qwen 2.5 Math 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Math 7B run at on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 Math 7B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.

Can RTX 4060 Ti 8GB run Qwen 2.5 Math 7B for coding?

For coding workloads, Qwen 2.5 Math 7B on RTX 4060 Ti 8GB receives a B grade with 49.2 tok/s and 4K context.

What context window can Qwen 2.5 Math 7B use on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, Qwen 2.5 Math 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 8GB See all hardware for Qwen 2.5 Math 7B

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