Gemma4 E4b Toolcall V01 Lora — Hardware Requirements & GPU Compatibility
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Gemma4 E4b Toolcall V01 Lora is a 4B-parameter open language model from roshangrewal in the Gemma 4 family. At BF16 it needs about 8.80 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (BF16 · 16K)Best on 24 GB (BF16 · ~75 tok/s · 32K)Macs from 16 GB (BF16 · ~16 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma4 E4b Toolcall V01 Lora
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 roshangrewal/gemma4-e4b-toolcall-v01-lora --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 5070 Ti (16 GB) (5 GB headroom) · BF16
- Generation speed
- ~66 tok/s
- generation speed
- Cost per 1M output tokens
- $0.71
- per 1M output tokens
How Much VRAM Does Gemma4 E4b Toolcall V01 Lora Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 8.8 GB | 13.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~75 tok/s |
Selected BF16
Brain floating point 1610.9GBat 16K context
- Weights
- 8.2 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 13.1 GB free · ~75 tok/s
≥50 tok/s needs ≥676 GB/s · RTX 5090 ~132, RTX 3090 Ti ~75
Which Hardware Can Run Gemma4 E4b Toolcall V01 Lora?
BF16 · 8.8 GBGemma4 E4b Toolcall V01 Lora (BF16) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Gemma4 E4b Toolcall V01 Lora, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 1.1 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~67Simulate output on RTX 3080 TiRTX 5070~50Simulate output on RTX 5070RTX 4070~37Simulate output on RTX 407020 GB
fits · 9.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~55Simulate output on RX 7900 XT32–40 GB
fits · 21.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~132Simulate output on RTX 5090M5 Max36 GB~37Simulate output on Mac Studio M5 Max (36 GB)M532 GB~12Simulate output on MacBook Air 13" M5 (32 GB)Orin~15Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~10Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma4 E4b Toolcall V01 Lora
Gemma 4 family · 35 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Spark X2.6 4BXHToken | — | 4.3 GB | ~221 | same size |
| NVIDIA Nemotron Labs Dory1.0 4B BF16NVIDIA | — | 5.7 GB | ~214 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Qwen3 4B Instruct 2507Alibaba | — | 4.2 GB | ~226 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
| Phi 4 Mini ReasoningMicrosoft | — | 4.8 GB | ~228 | same size |
Specifications
- Publisher
- roshangrewal
- Family
- Gemma 4
- Parameters
- 4B
- Release Date
- 2026-06-02
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Gemma4 E4b Toolcall V01 Lora need?
Gemma4 E4b Toolcall V01 Lora requires 8.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 16 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.8 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
Can I run Gemma4 E4b Toolcall V01 Lora on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Gemma4 E4b Toolcall V01 Lora. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 8.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma4 E4b Toolcall V01 Lora locally?
Yes — Gemma4 E4b Toolcall V01 Lora can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma4 E4b Toolcall V01 Lora?
At BF16, Gemma4 E4b Toolcall V01 Lora can reach ~546 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~75 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 ÷ 8.8 × 0.65 = ~591 tok/s
Estimated speed at BF16 (8.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma4 E4b Toolcall V01 Lora?
At BF16, the download is about 8.00 GB.
Which GPUs can run Gemma4 E4b Toolcall V01 Lora?
40 consumer GPUs can run Gemma4 E4b Toolcall V01 Lora at BF16. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma4 E4b Toolcall V01 Lora?
150 devices with unified memory can run Gemma4 E4b Toolcall V01 Lora at BF16 (8.8 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.