Can WizardMath 7B run on NVIDIA L4 24GB?

YES — Runs Great

B69Good

Estimated from fit model

WizardMath 7B needs ~9.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.8 GB, 49.1 tok/s, Runs well

9.8 GB required24.0 GB available

41% VRAM used

Fit status

Runs well

Decode

49.1 tok/s

TTFT

3944 ms

Safe context

Memory

9.8 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsWizardMath 7B on NVIDIA L4 24GB
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: 49.1 tok/s decode · 3.9s TTFT (warm) · 123 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	Runs well	49.1 tok/s	2151 ms	4K
Coding	B	Runs well	45.7 tok/s	4239 ms	4K
Agentic Coding	A	Runs well	49.1 tok/s	5736 ms	4K
Reasoning	B	Runs well	49.1 tok/s	4661 ms	4K
RAG	A	Runs well	49.1 tok/s	7170 ms	4K

Quantization options

How WizardMath 7B (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4	3.9 GB	Medium	B66
Q4_K_M	4	4.3 GB	Medium	B66
Q5_K_M	5	5.0 GB	High	B66
Q6_K	6	5.7 GB	High	B66
Q8_0	8	7.5 GB	Very High	B68
F16Best for your GPU	16	14.3 GB	Maximum	A71

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

升级选项

能流畅运行 WizardMath 7B 的硬件

RTX 5090 32GB经济之选

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 100%.98 tok/s 解码

Raises estimated decode speed by about 100%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GB最佳性价比

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 100%.98 tok/s 解码

Raises estimated decode speed by about 100%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA 升级

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 100%.98 tok/s 解码

Raises estimated decode speed by about 100%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run WizardMath 7B?

Yes, NVIDIA L4 24GB can run WizardMath 7B with a B grade (Runs well). Expected decode speed: 45.7 tok/s.

How much VRAM does WizardMath 7B need?

WizardMath 7B (7B parameters) requires approximately 9.8 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, WizardMath 7B achieves approximately 45.7 tokens per second decode speed with a time-to-first-token of 4239ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run WizardMath 7B for coding?

For coding workloads, WizardMath 7B on NVIDIA L4 24GB receives a B grade with 45.7 tok/s and 4K context.

What context window can WizardMath 7B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 NVIDIA L4 24GB See all hardware for WizardMath 7B

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