Can Qwen 2.5 Math 7B run on GTX 1660 Super 6GB?

Q: What should I upgrade first if Qwen 2.5 Math 7B feels slow on GTX 1660 Super 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

C44Usable

Estimated from fit model

Qwen 2.5 Math 7B needs ~6.6 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 6.6 GB, 27.6 tok/s, Very compromised (needs ~0.4 GB host RAM)

6.6 GB required6.0 GB available

110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

27.6 tok/s

TTFT

7021 ms

Safe context

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Math 7B on GTX 1660 Super 6GB
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: 27.6 tok/s decode · 7.0s TTFT (warm) · 69 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.1 GB host RAM)	31.9 tok/s	3308 ms	4K
Coding	C	Very compromised (needs ~0.4 GB host RAM)	27.6 tok/s	7021 ms	4K
Agentic Coding	F	Too heavy	21.1 tok/s	13329 ms	4K
Reasoning	C	Very compromised (needs ~0.4 GB host RAM)	27.6 tok/s	8297 ms	4K
RAG	F	Too heavy	21.1 tok/s	16662 ms

Inference speed

Qwen 2.5 Math 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Math 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

Quantization options

How Qwen 2.5 Math 7B (7B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B58
Q3_K_SBest for your GPU	3	3.4 GB	Low	B58

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

Upgrade options

Hardware that runs Qwen 2.5 Math 7B well

RTX 3050 8GBBudget pick

8 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.31.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$249 MSRP

RTX 5060 8GBBest value

8 GB VRAM (+2)448 GB/s (+112)

Removes host-memory offload, which is usually the single biggest latency and throughput win.69.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 152%.

~$299 MSRP

RTX 5050 8GBNVIDIA upgrade

8 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.47.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 73%.

~$299 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run Qwen 2.5 Math 7B?

Yes, GTX 1660 Super 6GB can run Qwen 2.5 Math 7B with a C grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 27.6 tok/s.

How much VRAM does Qwen 2.5 Math 7B need?

Qwen 2.5 Math 7B (7B parameters) requires approximately 6.6 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 GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, Qwen 2.5 Math 7B achieves approximately 27.6 tokens per second decode speed with a time-to-first-token of 7021ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run Qwen 2.5 Math 7B for coding?

For coding workloads, Qwen 2.5 Math 7B on GTX 1660 Super 6GB receives a C grade with 27.6 tok/s and 4K context.

What context window can Qwen 2.5 Math 7B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, 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.

What should I upgrade first if Qwen 2.5 Math 7B feels slow on GTX 1660 Super 6GB?

See all results for GTX 1660 Super 6GB See all hardware for Qwen 2.5 Math 7B

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