Can Phi-4-reasoning-plus 14B run on RTX 4070 Laptop 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Phi-4-reasoning-plus 14B needs ~14.0 GB but RTX 4070 Laptop 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) — 14.0 GB, exceeds 8.0 GB available

14.0 GB required8.0 GB available

175% VRAM needed

6.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.4 tok/s

TTFT

36045 ms

Safe context

Memory

14.0 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.0 GB

KV Cache3.1 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsPhi-4-reasoning-plus 14B on RTX 4070 Laptop 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: 5.4 tok/s decode · 36.0s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.0 GB, but this setup only exposes 8.0 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	6.8 tok/s	15426 ms	4K
Coding	F	Too heavy	5.4 tok/s	36045 ms	4K
Agentic Coding	F	Too heavy	3.5 tok/s	79365 ms	4K
Reasoning	F	Too heavy	5.4 tok/s	42599 ms	4K
RAG	F	Too heavy	3.5 tok/s	99207 ms	4K

Inference speed

Phi-4-reasoning-plus 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi-4-reasoning-plus 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 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	143.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	91.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	78.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.5	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	66.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	52.7	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	37.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	37.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	26.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	24.9	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	14.6	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.5	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 Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.7 GB	Low	F0
Q3_K_S	3	7.2 GB	Low	F0
NVFP4	4	8.2 GB	Medium	F0
Q4_K_M	4	9.0 GB	Medium	F0
Q5_K_M	5	10.6 GB	High	F0
Q6_K	6	12.1 GB	High	F0
Q8_0	8	15.7 GB	Very High	F0
F16	16	30.1 GB	Maximum	F0

Opciones de mejora

Hardware que ejecuta bien Phi-4-reasoning-plus 14B

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.33.3 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 16GBMejor relación calidad-precio

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

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.25.2 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

RTX 5070 Ti 16GBMejora NVIDIA

16 GB VRAM (+8)896 GB/s (+640)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.68.7 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.

~$749 MSRP

Frequently asked questions

Can RTX 4070 Laptop 8GB run Phi-4-reasoning-plus 14B?

No, Phi-4-reasoning-plus 14B requires more memory than RTX 4070 Laptop 8GB provides.

How much VRAM does Phi-4-reasoning-plus 14B need?

Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4-reasoning-plus 14B?

The recommended quantization for Phi-4-reasoning-plus 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4-reasoning-plus 14B run at on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Phi-4-reasoning-plus 14B achieves approximately 5.4 tokens per second decode speed with a time-to-first-token of 36045ms using Q4_K_M quantization.

Can RTX 4070 Laptop 8GB run Phi-4-reasoning-plus 14B for coding?

For coding workloads, Phi-4-reasoning-plus 14B on RTX 4070 Laptop 8GB receives a F grade with 5.4 tok/s and 4K context.

What context window can Phi-4-reasoning-plus 14B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, Phi-4-reasoning-plus 14B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Phi-4-reasoning-plus 14B feels slow on RTX 4070 Laptop 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 RTX 4070 Laptop 8GB See all hardware for Phi-4-reasoning-plus 14B

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