Can Qwen 2.5 Math 72B run on RTX 3090 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 2.5 Math 72B needs ~52.1 GB but RTX 3090 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity

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) — 52.1 GB, exceeds 24.0 GB available

52.1 GB required24.0 GB available

217% VRAM needed

28.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

52.1 GB / 24.0 GB

Offload

50%

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 Math 72B on RTX 3090 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 52.1 GB, but this setup only exposes 24.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Qwen 2.5 Math 72B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Math 72B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~17 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	16.7	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	11.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	8.8	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	8.1	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	7.1	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	5.3	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

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 Qwen 2.5 Math 72B (72B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	F0
Q3_K_S	3	35.3 GB	Low	F0
NVFP4	4	40.3 GB	Medium	F0
Q4_K_M	4	43.9 GB	Medium	F0
Q5_K_M	5	51.8 GB	High	F0
Q6_K	6	59.0 GB	High	F0
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Math 72B

RTX A6000 48GBMejor relación calidad-precio

48 GB VRAM (+24)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.3 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 315%.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMejora NVIDIA

48 GB VRAM (+24)1344 GB/s (+408)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.16.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 725%.

~$4,999 MSRP

NVIDIA A16 64GBOpción económica

64 GB VRAM (+40)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.11.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$6,500 MSRP

NVIDIA H100 80GBMayor salto

80 GB VRAM (+56)3350 GB/s (+2414)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.69.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$40,000 MSRP

Frequently asked questions

Can RTX 3090 24GB run Qwen 2.5 Math 72B?

No, Qwen 2.5 Math 72B requires more memory than RTX 3090 24GB provides.

How much VRAM does Qwen 2.5 Math 72B need?

Qwen 2.5 Math 72B (72B parameters) requires approximately 52.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Math 72B?

The recommended quantization for Qwen 2.5 Math 72B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Math 72B run at on RTX 3090 24GB?

On RTX 3090 24GB, Qwen 2.5 Math 72B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 3090 24GB run Qwen 2.5 Math 72B for coding?

For coding workloads, Qwen 2.5 Math 72B on RTX 3090 24GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Qwen 2.5 Math 72B use on RTX 3090 24GB?

On RTX 3090 24GB, Qwen 2.5 Math 72B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Math 72B feels slow on RTX 3090 24GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RTX 3090 24GB See all hardware for Qwen 2.5 Math 72B

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