Can Qwen 2.5 Math 72B run on NVIDIA A16 64GB?

YES — Tight Fit

B61Good

Estimated from fit model

Qwen 2.5 Math 72B needs ~56.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 56.1 GB, 11.6 tok/s, Tight fit

56.1 GB required64.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

11.6 tok/s

TTFT

16707 ms

Safe context

Memory

56.1 GB / 64.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 72B on NVIDIA A16 64GB
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: 11.6 tok/s decode · 16.7s TTFT (warm) · 29 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	Tight fit	11.6 tok/s	9113 ms	4K
Coding	B	Tight fit	11.6 tok/s	16707 ms	4K
Agentic Coding	B	Runs with offload	11.6 tok/s	24301 ms	4K
Reasoning	B	Tight fit	11.6 tok/s	19744 ms	4K
RAG	B	Runs with offload	11.6 tok/s	30376 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 NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	B60
Q3_K_S	3	35.3 GB	Low	B61
NVFP4	4	40.3 GB	Medium	B61
Q4_K_M	4	43.9 GB	Medium	B61
Q5_K_MBest for your GPU	5	51.8 GB	High	B61
Q6_K	6	59.0 GB	High	F0
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Math 72B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen2.5-Math-72B-Instruct" \
  --hf-file "Qwen2.5-Math-72B-Instruct-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opções de upgrade

Hardware que roda bem Qwen 2.5 Math 72B

RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.37.3 tok/s decodificação

Raises estimated decode speed by about 222%.

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

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBMelhor custo-benefício

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 186%.33.2 tok/s decodificação

Raises estimated decode speed by about 186%.

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

~$9,999 MSRP

NVIDIA H20 96GBUpgrade NVIDIA

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 591%.80.2 tok/s decodificação

Raises estimated decode speed by about 591%.

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

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Qwen 2.5 Math 72B?

Yes, NVIDIA A16 64GB can run Qwen 2.5 Math 72B with a B grade (Tight fit). Expected decode speed: 11.6 tok/s.

How much VRAM does Qwen 2.5 Math 72B need?

Qwen 2.5 Math 72B (72B parameters) requires approximately 56.1 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Math 72B achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16707ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen 2.5 Math 72B for coding?

For coding workloads, Qwen 2.5 Math 72B on NVIDIA A16 64GB receives a B grade with 11.6 tok/s and 4K context.

What context window can Qwen 2.5 Math 72B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.

See all results for NVIDIA A16 64GB See all hardware for Qwen 2.5 Math 72B

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