Can Surface Laptop Ultra · RTX Spark N1X (18-core) · 24 GB run Phi-4 (14B)?
24 GB unified · 300 GB/s memory bandwidth · Phi-4 (14B) has 14.7B parameters.
Short answer
Yes, excellent for chat; borderline for coding in a repository.
Best quantization for chat: Q4_K_M (8.3 GB of weights) on Ollama. Generation: 16–25 tok/s, leaving about 4.7 GB free for your other apps.
How it rates for each workload
Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.
| Workload | Verdict | Quant | Generation | First reply | Context | |
|---|---|---|---|---|---|---|
| Chatwith Open WebUI | Excellent | Q4_K_M | 16–25 tok/s | under 1 s | 8K | Details |
| Coding questionswith Continue | Acceptable | Q3_K_M | 18–30 tok/s | ≈2 s | 16K | Details |
| Coding in a repositorywith Aider | Borderline | Q4_K_M | 12–20 tok/s | ≈5 s | 16K | Details |
| Agentic codingwith OpenCode | Borderline | Q5_K_M | 11–18 tok/s | ≈5 s | 16K | Details |
| Long documents (64K)with Open WebUI | Borderline | Q5_K_M | 11–18 tok/s | ≈5 s | 16K | Details |
Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate
Get it running
Copy-paste commands for the Q3_K_M build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.
ollama run hf.co/unsloth/phi-4-GGUF:Q3_K_M
Downloads 7.19 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.
Other models for Surface Laptop Ultra · RTX Spark N1X (18-core) · 24 GB
The best-rated models for chat on this machine.
- gpt-oss-20b50–85 tok/sExcellent
- MiniCPM5 2B55–90 tok/sExcellent
- Gemma 4 26B-A4B60–95 tok/sExcellent
- Qwen3.5 4B35–55 tok/sExcellent
- Qwen3.5 9B18–30 tok/sExcellent
- Gemma 4 12B20–35 tok/sExcellent
Cheapest machines for Phi-4 (14B)
Hardware rated comfortable or better for chat, lowest price first.
- Mac mini M6 24 GB$1,299 · 10–16 tok/sComfortable
- Desktop PC · GeForce RTX 5060 Ti 16GB · 32 GB RAM$1,330 · 25–35 tok/sExcellent
- Desktop PC · GeForce RTX 4060 Ti 16GB · 32 GB RAM$1,350 · 15–25 tok/sExcellent
- Mac mini M4 Pro 24 GB$1,399 · 19–25 tok/sExcellent
- Desktop PC · Radeon RX 9070 XT · 32 GB RAM$1,500 · 40–55 tok/sExcellent
- Desktop PC · Intel Arc Pro B60 24GB · 32 GB RAM$1,500 · 16–25 tok/sExcellent
Same chip, other memory: 128 GB
Different tool, context size or workload?
These pages use typical settings. The full check models your exact setup.