Can WizardMath 7B run on RTX 3080 10GB?

YES — Runs Great

A77Great

Estimated from fit model

WizardMath 7B needs ~8.1 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.1 GB, 84.0 tok/s, Runs well

8.1 GB required10.0 GB available

81% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

Memory

8.1 GB / 10.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsWizardMath 7B on RTX 3080 10GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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	A	Runs well	84.0 tok/s	1257 ms	4K
Coding	A	Runs well	84.0 tok/s	2305 ms	4K
Agentic Coding	A	Runs with offload (needs ~0 GB host RAM)	84.0 tok/s	3352 ms	4K
Reasoning	A	Runs well	84.0 tok/s	2724 ms	4K
RAG	A	Runs with offload (needs ~0 GB host RAM)	84.0 tok/s	4190 ms	4K

Inference speed

WizardMath 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for WizardMath 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	95.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	94.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	94.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	59.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	48.7	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.0	Offloads

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 WizardMath 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A71
Q3_K_S	3	3.4 GB	Low	A72
NVFP4	4	3.9 GB	Medium	A73
Q4_K_M	4	4.3 GB	Medium	A74
Q5_K_M	5	5.0 GB	High	A73
Q6_KBest for your GPU	6	5.7 GB	High	A73
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 3080 10GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	91.2 tok/s
Qwen 3 8B	8B	S	96 tok/s
Nemotron Nano 8B	8B	S	96 tok/s
InternVL2 8B	8B	S	96 tok/s
Ministral 3 8B	8B	A	96 tok/s

Frequently asked questions

Can RTX 3080 10GB run WizardMath 7B?

Yes, RTX 3080 10GB can run WizardMath 7B with a A grade (Runs well). Expected decode speed: 84.0 tok/s.

How much VRAM does WizardMath 7B need?

WizardMath 7B (7B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.

What is the best quantization for WizardMath 7B?

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

What speed will WizardMath 7B run at on RTX 3080 10GB?

On RTX 3080 10GB, WizardMath 7B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can RTX 3080 10GB run WizardMath 7B for coding?

For coding workloads, WizardMath 7B on RTX 3080 10GB receives a A grade with 84.0 tok/s and 4K context.

What context window can WizardMath 7B use on RTX 3080 10GB?

On RTX 3080 10GB, WizardMath 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 3080 10GB See all hardware for WizardMath 7B

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