Phi-4-reasoning-plus 14B needs ~10.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q2_K quantization, expect ~22 tok/s.
Operating mode
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.
Select quantization to explore
3.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.6 tok/s
TTFT
20203 ms
Safe context
4K
Memory
13.9 GB / 10.0 GB
Offload
30%
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.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
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.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 12.1 tok/s | 8696 ms | 4K |
| Coding | F | Too heavy | 9.6 tok/s | 20203 ms | 4K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 44033 ms | 4K |
| Reasoning | F | Too heavy | 9.6 tok/s | 23876 ms | 4K |
| RAG | F | Too heavy | 6.4 tok/s | 55041 ms | 4K |
Inference speed
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.
How Phi-4-reasoning-plus 14B (14.699999809265137B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.7 GB | Low | S92 |
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 |
Copy-paste commands to run Phi-4-reasoning-plus 14B on your machine.
Run
ollama run phi4-reasoningUpgrade-Optionen
Intel Arc B580 12GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 50%.
ca. $249 MSRP
Intel Arc A770 16GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $349 MSRP
Intel Arc Pro B50 16GBIntel-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $399 MSRP
RTX 4090 24GBGrößter SprungMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,599 MSRP
Yes, Intel Arc B570 10GB can run Phi-4-reasoning-plus 14B at Q2_K quantization (Runs with offload (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 13.9 GB which exceeds available memory, but at Q2_K it needs only 10.7 GB. Expected decode speed: 21.8 tok/s.
Phi-4-reasoning-plus 14B (14.699999809265137B parameters) requires approximately 13.9 GB at Q4_K_M quantization. On Intel Arc B570 10GB, it fits at Q2_K using 10.7 GB.
The recommended quantization is Q4_K_M, but on Intel Arc B570 10GB the best fitting quantization is Q2_K, which uses 10.7 GB.
On Intel Arc B570 10GB, Phi-4-reasoning-plus 14B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8861ms using Q2_K quantization.
For coding workloads, Phi-4-reasoning-plus 14B on Intel Arc B570 10GB receives a F grade with 9.6 tok/s and 4K context.
On Intel Arc B570 10GB, Phi-4-reasoning-plus 14B can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is 33K, but available memory constrains the safe maximum.
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.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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