Can WizardMath 7B run on RTX 3060 Ti 8GB?

YES — With Offload

A73Great

○Estimated from fit model

WizardMath 7B needs ~7.9 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 7.9 GB, 64.4 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

64.4 tok/s

TTFT

3005 ms

Safe context

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsWizardMath 7B on RTX 3060 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: 64.4 tok/s decode · 3.0s TTFT (warm) · 161 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	64.4 tok/s	1639 ms	4K
Coding	A	Runs with offload	64.4 tok/s	3005 ms	4K
Agentic Coding	F	Too heavy	31.0 tok/s	9081 ms	4K
Reasoning	A	Runs with offload	64.4 tok/s	3551 ms	4K
RAG	F	Too heavy	31.0 tok/s	11351 ms	4K

Quantization options

How WizardMath 7B (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4	3.9 GB	Medium	A74
Q4_K_M	4	4.3 GB	Medium	A74
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 WizardMath 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
 --hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
 -c 4096 -ngl 99

Your hardware

More models your RTX 3060 Ti 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	A	27.4 tok/s
👁 Alibaba Qwen 3 8B	8B	A	29.9 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	36.9 tok/s
👁 InternLM InternVL2 8B	8B	A	36.9 tok/s
👁 Mistral Ministral 3 8B	8B	A	34.8 tok/s

Frequently asked questions

See all results for RTX 3060 Ti 8GB See all hardware for WizardMath 7B

URL: https://willitrunai.com/can-run/wizard-math-7b-on-rtx-3060-ti-8gb