Tofu Ft Phi 1.5 — Hardware Requirements & GPU Compatibility
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Tofu Ft Phi 1.5 is a 1.4B-parameter open language model from locuslab in the Phi family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 1.55 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~423 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~46 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Tofu Ft Phi 1.5
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve locuslab/tofu_ft_phi-1.5 --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~383 tok/s
- generation speed
- Cost per 1M output tokens
- $0.089
- per 1M output tokens
How Much VRAM Does Tofu Ft Phi 1.5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~500 tok/s | |
| 1.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~471 tok/s | |
| 1.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~423 tok/s | |
| 1.7 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~383 tok/s | |
| 1.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~350 tok/s | |
| 2.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~309 tok/s | |
| 3.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~185 tok/s |
Selected Q4_K_M
4-bit medium quantization1.6GBat 2K context
- Weights
- 0.9 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.5 GB free · ~423 tok/s
≥50 tok/s needs ≥119 GB/s · RTX 5090 ~752, RTX 3090 Ti ~423
Which Hardware Can Run Tofu Ft Phi 1.5?
Q4_K_M · 1.6 GBTofu Ft Phi 1.5 (Q4_K_M) requires 1.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Tofu Ft Phi 1.5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.5 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~319Simulate output on RTX 3080RTX 2080 Ti~258Simulate output on RTX 2080 TiRTX 3070 Ti~255Simulate output on RTX 3070 TiM3*8 GB~46Simulate output on iMac 24" M3 (8 GB)Orin Nano~43Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~31Simulate output on iPhone 1720 GB
fits · 18.4 GB free · up to 2K ctx
1 GPU
RX 7900 XT~310Simulate output on RX 7900 XT32–40 GB
fits · 30.4 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~752Simulate output on RTX 5090M5 Max36 GB~208Simulate output on Mac Studio M5 Max (36 GB)M532 GB~69Simulate output on MacBook Air 13" M5 (32 GB)Orin~86Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~57Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Tofu Ft Phi 1.5
Phi family · 9 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Inkling Small DSpark PreviewRadixArk | — | 1.5 GB | ~555 | same size |
| Sweep Next Edit 1.5BXenova | — | 1.6 GB | ~551 | same size |
| Ouro 1.4BByteDance | — | 6.4 GB | ~184 | same size |
| OLMo 2 0425 1B InstructAllen AI | — | 1.7 GB | ~449 | same size |
| Granite 3.0 1B A400m InstructIBM | — | 1.3 GB | ~524 | same size |
| Transformer 1.3B 100Bfla-hub | — | 3 GB | ~218 | same size |
Specifications
- Publisher
- locuslab
- Family
- Phi
- Parameters
- 1.4B
- Architecture
- PhiForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 51,200
- Release Date
- 2024-01-31
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Tofu Ft Phi 1.5 need?
Tofu Ft Phi 1.5 requires 1.6 GB of VRAM at Q4_K_M, or 3.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.4B × 4.8 bits ÷ 8 = 0.9 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Tofu Ft Phi 1.5?
For Tofu Ft Phi 1.5, Q4_K_M (1.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Tofu Ft Phi 1.5 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Tofu Ft Phi 1.5. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Tofu Ft Phi 1.5 locally?
Yes — Tofu Ft Phi 1.5 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Tofu Ft Phi 1.5?
At Q4_K_M, Tofu Ft Phi 1.5 can reach ~3097 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~423 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.55 × 0.65 = ~3355 tok/s
Estimated speed at Q4_K_M (1.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Tofu Ft Phi 1.5?
At Q4_K_M, the download is about 0.85 GB. The full-precision BF16 version is 2.84 GB. The smallest option (Q2_K) is 0.60 GB.
Which GPUs can run Tofu Ft Phi 1.5?
52 consumer GPUs can run Tofu Ft Phi 1.5 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Tofu Ft Phi 1.5?
163 devices with unified memory can run Tofu Ft Phi 1.5 at Q4_K_M (1.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.