Can Qwen 3 14B run on GTX 1070 Ti 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Qwen 3 14B needs ~13.0 GB but GTX 1070 Ti 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: 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) — 13.0 GB, exceeds 8.0 GB available

13.0 GB required8.0 GB available

163% VRAM needed

5.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.8 tok/s

TTFT

40229 ms

Safe context

Memory

13.0 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights8.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 14B on GTX 1070 Ti 8GB
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: 4.8 tok/s decode · 40.2s TTFT (warm) · 12 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.0 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

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	6.0 tok/s	17665 ms	4K
Coding	F	Too heavy	4.8 tok/s	40229 ms	4K
Agentic Coding	F	Too heavy	3.3 tok/s	85433 ms	4K
Reasoning	F	Too heavy	4.8 tok/s	47544 ms	4K
RAG	F	Too heavy	3.3 tok/s	106791 ms	4K

Inference speed

Qwen 3 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 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	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	88.4	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	82.9	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	34.2	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	18.4	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.8	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 3 14B (14B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4	7.8 GB	Medium	F0
Q4_K_M	4	8.5 GB	Medium	F0
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Qwen 3 14B

RTX 3060 12GBMejor relación calidad-precio

12 GB VRAM (+4)360 GB/s (+104)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.17.9 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 273%.

~$329 MSRP

RTX 5060 Ti 16GBOpción económica

16 GB VRAM (+8)448 GB/s (+192)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.35.1 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.

~$449 MSRP

RTX 4060 Ti 16GBMejora NVIDIA

16 GB VRAM (+8)288 GB/s (+32)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.26.6 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.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run Qwen 3 14B?

No, Qwen 3 14B requires more memory than GTX 1070 Ti 8GB provides.

How much VRAM does Qwen 3 14B need?

Qwen 3 14B (14B parameters) requires approximately 13.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 14B?

The recommended quantization for Qwen 3 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 14B run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Qwen 3 14B achieves approximately 4.8 tokens per second decode speed with a time-to-first-token of 40229ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run Qwen 3 14B for coding?

For coding workloads, Qwen 3 14B on GTX 1070 Ti 8GB receives a F grade with 4.8 tok/s and 4K context.

What context window can Qwen 3 14B use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Qwen 3 14B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 14B feels slow on GTX 1070 Ti 8GB?

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 GTX 1070 Ti 8GB See all hardware for Qwen 3 14B

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