Can Qwen 2.5 Math 7B run on NVIDIA L4 24GB?

YES — Runs Great

C52Usable

Estimated from fit model

Qwen 2.5 Math 7B needs ~8.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~50 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) — 8.7 GB, 49.6 tok/s, Runs well

8.7 GB required24.0 GB available

36% VRAM used

Fit status

Runs well

Decode

49.6 tok/s

TTFT

3905 ms

Safe context

Memory

8.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 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: 49.6 tok/s decode · 3.9s TTFT (warm) · 124 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	C	Runs well	49.6 tok/s	2130 ms	4K
Coding	C	Runs well	49.6 tok/s	3905 ms	4K
Agentic Coding	C	Runs well	49.6 tok/s	5680 ms	4K
Reasoning	C	Runs well	49.6 tok/s	4615 ms	4K
RAG	C	Runs well	49.6 tok/s	7099 ms	4K

Quantization options

How Qwen 2.5 Math 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	C49
Q3_K_S	3	3.4 GB	Low	C49
NVFP4	4	3.9 GB	Medium	C49
Q4_K_M	4	4.3 GB	Medium	C49
Q5_K_M	5	5.0 GB	High	C50
Q6_K	6	5.7 GB	High	C50
Q8_0	8	7.5 GB	Very High	C51
F16Best for your GPU	16	14.3 GB	Maximum	C54

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

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Math 7B

RTX 5090 32GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 98%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 98%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Qwen 2.5 Math 7B?

Yes, NVIDIA L4 24GB can run Qwen 2.5 Math 7B with a C grade (Runs well). Expected decode speed: 49.6 tok/s.

How much VRAM does Qwen 2.5 Math 7B need?

Qwen 2.5 Math 7B (7B parameters) requires approximately 8.7 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Qwen 2.5 Math 7B achieves approximately 49.6 tokens per second decode speed with a time-to-first-token of 3905ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Qwen 2.5 Math 7B for coding?

For coding workloads, Qwen 2.5 Math 7B on NVIDIA L4 24GB receives a C grade with 49.6 tok/s and 4K context.

What context window can Qwen 2.5 Math 7B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, 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 NVIDIA L4 24GB See all hardware for Qwen 2.5 Math 7B

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