willitrun·ai

Can Phi 4 reasoning vision 15B run on RTX 4060 Ti 16GB?

YES — Tight Fit

C49Usable
Estimated from fit model

Phi 4 reasoning vision 15B needs ~13.4 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 13.4 GB, 23.5 tok/s, Tight fit
13.4 GB required16.0 GB available
84% VRAM used

Fit status

Tight fit

Decode

23.5 tok/s

TTFT

8239 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB
KV Cache1.8 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsPhi 4 reasoning vision 15B on RTX 4060 Ti 16GB
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: 23.5 tok/s decode · 8.2s TTFT (warm) · 59 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well23.5 tok/s4494 ms40K
CodingCTight fit23.5 tok/s8239 ms40K
Agentic CodingCTight fit23.5 tok/s11984 ms40K
ReasoningCTight fit23.5 tok/s9737 ms40K
RAGCTight fit23.5 tok/s14980 ms40K

Inference speed

Phi 4 reasoning vision 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Phi 4 reasoning vision 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M131.2Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M83.7Fits
RX 7900 XTX 24GB
24 GBQ4_K_M75.5Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M71.6Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M68.3Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M60.9Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M50.7Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M48.1Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M34.7Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M34.7Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M26.7Heavy offload
MacBook Pro M3 Max 64GB
64 GBQ4_K_M26.2Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M24.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M21.2Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M15.7Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M5.9Too 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 vision 15B (15B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowC50
Q3_K_S
3
7.4 GB
LowC51
NVFP4
4
8.4 GB
MediumC51
Q4_K_M
4
9.2 GB
MediumC51
Q5_K_M
5
10.8 GB
HighC51
Q6_KBest for your GPU
6
12.3 GB
HighC50
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

Get started

Copy-paste commands to run Phi 4 reasoning vision 15B on your machine.

Run

lms load hf-jamesburton--phi-4-reasoning-vision-15b-gguf && lms server start

Opções de upgrade

Hardware que roda bem Phi 4 reasoning vision 15B

NVIDIARTX 4000 Ada 20GBOpção econômica
20 GB VRAM (+4)360 GB/s (+72)
C
Raises estimated decode speed by about 31%.30.7 tok/s decodificação

Raises estimated decode speed by about 31%.

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

~$1,250 MSRP

NVIDIARTX 3090 24GBMelhor custo-benefício
24 GB VRAM (+8)936 GB/s (+648)
C
Raises estimated decode speed by about 191%.68.3 tok/s decodificação

Raises estimated decode speed by about 191%.

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

~$1,499 MSRP

NVIDIARTX 4090 24GBUpgrade NVIDIA
24 GB VRAM (+8)1008 GB/s (+720)
C
Raises estimated decode speed by about 264%.85.6 tok/s decodificação

Raises estimated decode speed by about 264%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run Phi 4 reasoning vision 15B?

Yes, RTX 4060 Ti 16GB can run Phi 4 reasoning vision 15B with a C grade (Tight fit). Expected decode speed: 23.5 tok/s.

How much VRAM does Phi 4 reasoning vision 15B need?

Phi 4 reasoning vision 15B (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi 4 reasoning vision 15B?

The recommended quantization for Phi 4 reasoning vision 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi 4 reasoning vision 15B run at on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, Phi 4 reasoning vision 15B achieves approximately 23.5 tokens per second decode speed with a time-to-first-token of 8239ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run Phi 4 reasoning vision 15B for coding?

For coding workloads, Phi 4 reasoning vision 15B on RTX 4060 Ti 16GB receives a C grade with 23.5 tok/s and 40K context.

What context window can Phi 4 reasoning vision 15B use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, Phi 4 reasoning vision 15B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GBSee all hardware for Phi 4 reasoning vision 15B
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